Publications

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20% of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25% to 50%. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patient's suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 +/- 1.8% when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events.

JTD Keywords: Bayesian optimization algorithm (boa, Continuous wavelet transform (cwt), Convolutional, Extubation, Failur, Intensive-care-unit, Neural network (cnn) from scratch, Respiratory-distress-syndrome, Time-frequency analysis (tfa), Weaning

[No abstract available]

JTD Keywords: Breathing, Cost, Developed country, Developing countries, Developing country, Health care facility, Home monitoring, Human, Humans, Internet, Internet of things, Letter, Lowest income group, Lung function, Lung mechanics, Lung resistance, Mathematical model, Middle income country, Mobile applications, Non invasive procedure, Open source technology, Oscillometry, Pneumotachygraphy, Telemedicine

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

JTD Keywords: Acute respiratory failure, Artificial ventilation, Asthmatic-children, Breathing muscle, Clinical monitoring, Clinical practice, Clinical research, Consensus development, Data interpretation, Disease exacerbation, Drive, Electrode positioning, Electrode removal, Electromyography, Force, Home care, Human, Human diaphragm, Humans, Information processing, Inspiratory muscle training, Inspiratory muscles, Intensive care unit, Knowledge gap, Long term care, Mechanical ventilation, Medical procedures, Muscle contraction, Muscle fatigue, Muscle function, Muscle training, Muscle, skeletal, Muscle-activity, Noninvasive ventilation, Patient monitoring, Patient-ventilator asynchrony, Physiology, Prognosis, Quality of life, Reporting and data system, Respiratory failure, Respiratory muscles, Review, Severe exacerbations, Signal processing, Skeletal muscle, Standardization, Surface electromyography, Time factor

In recent years, metaplasmonic biosensors have emerged as a novel counterpart of well-established plasmonic biosensors based on thin metallic layers. Metaplasmonic biosensors offer high potential for sensor miniaturiza-tion, extreme sensitivity biosensing, and high multiplexing capabilities with detection methods free of coupling optical elements. These capabilities make metaplasmonic biosensors highly attractive for Point-of-Care and handled/portable devices or novel On-Chip devices; as a result, it has increased the number of prototypes and potential applications that emerged during the last years. One of the main challenges to achieving fully operative devices is the achievement of high-throughput biointerfaces for sensitive and selective biodetection in complex media. Despite the superior surface sensitivity achieved by metaplasmonic sensors compared to conventional plasmonic sensors based on metallic thin films, the main limitations to achieving high-throughput and multiplexed biosensing usually are associated with the sensitivity and selectivity of the bioin-terface and, as a consequence, their application to the direct analysis of real complex samples. This graphical review discusses the potential challenges and capabilities of different biofunctionalization strategies, biorecog-nition elements, and antifouling strategies to achieve scalable and high-throughput metaplasmonic biosensing for Point-of-Care devices and bioengineering applications like Organs-On-Chip.

JTD Keywords: Biointerfaces, Biosensing, Biosensors, Cell culture monitoring, Metaplasmonic, Nanoplasmonic, Organ-on-chip, Point-of-care

Aims: The aim of this rapid review and opinion paper is to present the state of the current evidence and present future directions for telehealth research and clinical service delivery for stroke rehabilitation. Methods: We conducted a rapid review of published trials in the field. We searched Medline using key terms related to stroke rehabilitation and telehealth or virtual care. We also searched clinical trial registers to identify key ongoing trials. Results: The evidence for telehealth to deliver stroke rehabilitation interventions is not strong and is predominantly based on small trials prone to Type 2 error. To move the field forward, we need to progress to trials of implementation that include measures of adoption and reach, as well as effectiveness. We also need to understand which outcome measures can be reliably measured remotely, and/or develop new ones. We present tools to assist with the deployment of telehealth for rehabilitation after stroke. Conclusion: The current, and likely long-term, pandemic means that we cannot wait for stronger evidence before implementing telehealth. As a research and clinical community, we owe it to people living with stroke internationally to investigate the best possible telehealth solutions for providing the highest quality rehabilitation.

JTD Keywords: rehabilitation, telehealth, Care, Feasibility, Rehabilitation, Telehealth, Trial, Virtual care

© 2020 Elsevier B.V. The current situation of the Covid-19 pandemic is indicated by a huge number of infections, high lethality, and rapid spread. These circumstances have stopped the activity of almost the entire world, affecting severely the global economy. A rapid diagnosis of the Covid-19 and a generalized testing protocol is essential to fight against the pandemic and to maintain health control in the population. Principal biosensing and diagnostic technologies used to monitor the spread of the SARS-CoV-2 are based on specific genomic analysis and rapid immune tests, both with different technology platforms that include advantages and disadvantages. Most of the in vitro diagnosis companies are competing to be the first on validating under different regulations their technology for placing their platforms for Covid-19 detection as fast as possible in this big international market. A comprehensive analysis of the commercialized technologies for the genomic based sensing and the antibody/antigen detection methods devoted to Covid-19 diagnosis is described in this review, which have been detailed and listed under different countries regulations. The effectiveness of the described technologies throughout the different stages of the disease and a critical comparison of the emerging technologies in the market to counterattack this pandemic have been discussed.

JTD Keywords: covid-19, in vitro diagnosis (ivd), lateral flow immunoassay, point of care (poc), reverse transcriptase polymerase chain reaction (rt-pcr), sars-cov-2, Covid-19, In vitro diagnosis (ivd), Lateral flow immunoassay, Point of care (poc), Reverse transcriptase polymerase chain reaction (rt-pcr), Sars-cov-2

Alzheimer's disease (AD) is a neurodegenerative disorder, characterized by a functional deterioration of the brain. Currently, there are selected biomarkers for its diagnosis in cerebrospinal fluid. However, its extraction has several disadvantages for the patient. Therefore, there is an urgent need for a detection method using sensitive and selective blood-based biomarkers. Kynurenic acid (KYNA) is a potential biomarker candidate for this purpose. The alteration of the KYNA levels in blood has been related with inflammatory processes in the brain, produced as a protective function when neurons are damaged. This paper describes a novel electrochemical immunosensor for KYNA detection, based on successive functionalization multi-electrode array. The resultant sensor was characterized by cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). The proposed biosensor detects KYNA within a linear calibration range from 10 pM to 100 nM using CA and EIS, obtaining a limit of detection (LOD) of 16.9 pM and 37.6 pM in buffer, respectively, being the lowest reported LOD for this biomarker. Moreover, to assess our device closer to the real application, the developed immunosensor was also tested under human serum matrix, obtaining an LOD of 391.71 pM for CA and 278.8 pM for EIS with diluted serum.

JTD Keywords: alzheimer’s disease (ad), blood analysis, chronoamperometry (ca), electrochemical biosensor, electrochemical impedance spectroscopy (eis), immunosensor, in vitro diagnosis (ivd), kynurenic acid (kyna), Alzheimer’s disease (ad), Blood analysis, Chronoamperometry (ca), Electrochemical biosensor, Electrochemical impedance spectroscopy (eis), Immunosensor, In vitro diagnosis (ivd), Kynurenic acid (kyna), Point of care diagnosis (poc)

Malaria, a parasitic infection caused by Plasmodium parasites and transmitted through the bite of infected female Anopheles mosquitos, is one of the main causes of mortality in many developing countries. Over 200 million new infections and nearly half a million deaths are reported each year, and more than three billion people are at risk of acquiring malaria worldwide. Nevertheless, most malaria cases could be cured if detected early. Malaria eradication is a top priority of the World Health Organisation. However, achieving this goal will require mass population screening and treatment, which will be hard to accomplish with current diagnostic tools. We report an electrochemical point-of-care device for the fast, simple and quantitative detection of Plasmodium falciparum lactate dehydrogenase (PfLDH) in whole blood samples. Sample analysis includes 5-min lysis to release intracellular parasites, and stirring for 5 more min with immuno-modified magnetic beads (MB) along with an immuno-modified signal amplifier. The rest of the magneto-immunoassay, including sample filtration, MB washing and electrochemical detection, is performed at a disposable paper electrode microfluidic device. The sensor provides PfLDH quantitation down to 2.47 ng mL−1 in spiked samples and for 0.006–1.5% parasitemias in Plasmodium-infected cultured red blood cells, and discrimination between healthy individuals and malaria patients presenting parasitemias >0.3%. Quantitative malaria diagnosis is attained with little user intervention, which is not achieved by other diagnostic methods.

JTD Keywords: Electrochemical magneto-immunosensor, Malaria quantitative diagnosis, Paper microfluidic electrode, Plasmodium LDH, Point-of-care (POC) testing

Background: Home hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making. Objective: The objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge. Methods: Predictive modeling of mortality and in-hospital admission was done in 2 different scenarios: at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients’ functional features, and population health risk assessment, were considered. Results: We studied 1925 HH/ED patients by applying a random forest classifier, as it showed the best performance. Average results of the area under the receiver operating characteristic curve (AUROC; sensitivity/specificity) for the prediction of mortality were 0.88 (0.81/0.76) and 0.89 (0.81/0.81) at entry and at home hospitalization discharge, respectively; the AUROC (sensitivity/specificity) values for in-hospital admission were 0.71 (0.67/0.64) and 0.70 (0.71/0.61) at entry and at home hospitalization discharge, respectively. Conclusions: The results showed potential for feeding clinical decision support systems aimed at supporting health professionals for inclusion of candidates into the HH/ED program, and have the capacity to guide transitions toward community-based care at HH discharge.

JTD Keywords: Home hospitalization, Health risk assessment, Predictive modeling, Chronic care, Integrated care, Modeling, Hospitalization, Health risk, Prediction, Mortality, Clinical decision support

Deep Vein Thrombosis and pulmonary embolism (DVT/PE) is one of the most common causes of unexpected death for hospital in-patients. D-dimer is used as a biomarker within blood for the diagnosis of DVT/PE. We report a low-cost microfluidic device with a conveniently biofunctionalised interdigitated electrode (IDE) array and a portable impedimetric reader as a point-of-care (POC) device for the detection of D-dimer to aid diagnosis of DVT/PE. The IDE array elements, fabricated on a polyethylenenaphtalate (PEN) substrate, are biofunctionalised in situ after assembly of the microfluidic device by electropolymerisation of a copolymer of polypyrrole to which is immobilised a histidine tag anti-D-Dimer antibody. The most consistent copolymer films were produced using chronopotentiometry with an applied current of 5μA for a period of 50 s using a two-electrode system. The quality of the biofunctionalisation was monitored using optical microscopy, chronopotentiometry curves and impedimetric analysis. Measurement of clinical plasma sample with a D-dimer at concentration of 437 ng/mL with 15 biofunctionalised IDE array electrodes gave a ratiometric percentage of sample reading against the blank with an average value of 124 ± 15 at 95% confidence. We have demonstrated the concept of a low cost disposable microfluidic device with a receptor functionalised on the IDE array for impedimetric detection towards POC diagnostics. Changing the receptor on the IDE array would allow this approach to be used for the direct detection of a wide range of analytes in a low cost manner.

JTD Keywords: Electropolymerisation, Impedimetric sensing, Interdigitated electrodes, Microfluidics, Point-of-care diagnostics

Bioimpedance has been widely studied as alternative to respiratory monitoring methods because of its linear relationship with respiratory volume during normal breathing. However, other body tissues and fluids contribute to the bioimpedance measurement. The objective of this study is to investigate the relevance of chest movement in thoracic bioimpedance contributions to evaluate the applicability of bioimpedance for respiratory monitoring. We measured airflow, bioimpedance at four electrode configurations and thoracic accelerometer data in 10 healthy subjects during inspiratory loading. This protocol permitted us to study the contributions during different levels of inspiratory muscle activity. We used chest movement and volume signals to characterize the bioimpedance signal using linear mixed-effect models and neural networks for each subject and level of muscle activity. The performance was evaluated using the Mean Average Percentage Errors for each respiratory cycle. The lowest errors corresponded to the combination of chest movement and volume for both linear models and neural networks. Particularly, neural networks presented lower errors (median below 4.29%). At high levels of muscle activity, the differences in model performance indicated an increased contribution of chest movement to the bioimpedance signal. Accordingly, chest movement contributed substantially to bioimpedance measurement and more notably at high muscle activity levels.

JTD Keywords: Diagnosis, Health care

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

JTD Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform

Even if the performance of a given ventilator has been evaluated in the laboratory under very well controlled conditions, inappropriate maintenance and lack of long-term stability and accuracy of the ventilator sensors may lead to ventilation errors in actual clinical practice. The aim of this study was to evaluate the actual performances of ventilators during clinical routines. A resistance (7.69 cmH(2)O/L/s) - elastance (100 mL/cmH(2)O) test lung equipped with pressure, flow, and oxygen concentration sensors was connected to the Y-piece of all the mechanical ventilators available for patients in four intensive care units (ICUs; n = 66). Ventilators were set to volume-controlled ventilation with tidal volume = 600 mL, respiratory rate = 20 breaths/minute, positive end-expiratory pressure (PEEP) = 8 cmH(2)O, and oxygen fraction = 0.5. The signals from the sensors were recorded to compute the ventilation parameters. The average standard deviation and range (min-max) of the ventilatory parameters were the following: inspired tidal volume = 607 36 (530-723) mL, expired tidal volume = 608 36 (530-728) mL, peak pressure = 20.8 2.3 (17.2-25.9) cmH(2)O, respiratory rate = 20.09 0.35 (19.5-21.6) breaths/minute, PEEP = 8.43 0.57 (7.26-10.8) cmH(2)O, oxygen fraction = 0.49 0.014 (0.41-0.53). The more error-prone parameters were the ones related to the measure of flow. In several cases, the actual delivered mechanical ventilation was considerably different from the set one, suggesting the need for improving quality control procedures for these machines.

JTD Keywords: Equipment and supplies, Medical devices, Intravenous, Quality assurance, Health care quality assessment, Ventilator accuracy, Ventilation error

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

JTD Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials

Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (T_I), expiratory time (T_E), breathing cycle duration (T_Tot), tidal volume (V_T), inspiratory fraction (T_I/T_Tot), half inspired flow (V_T/T_I), and rapid shallow index (f/V_T), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.

JTD Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers

Background: Home continuous positive airway pressure (CPAP) titration with automatic devices is not possible in a non-negligible percentage of patients with sleep apnea-hypopnea syndrome (SAHS). Objectives: To test the feasibility of a novel telemetric system for home CPAP titration. Methods: One-night home CPAP titration was carried out on 20 SAHS patients (56 +/- 3 years; BMI = 35 +/- 2 kg/m(2)). A telemetric unit, based on the conventional GPRS mobile phone network and connected to a commercial CPAP device, allowed the hospital technician to monitor flow, pressure and air leaks by remote control and titrate CPAP (elimination of apneas, hypopneas, flow limitation and snoring) in real time. After 1 week, a full hospital polysomnography was performed while the patient was subjected to the value of CPAP that was previously titrated at home via telemetry. Results: The home-titrated CPAP systematically improved patients' breathing: the apnea-hypopnea index and percentage of sleep time with arterial oxygen saturation below 90% were reduced from 58.1 +/- 5.1 to 3.8 +/- 0.6 events/h and from 19.8 +/- 1.1% to 4.4 +/- 0.7%, respectively. This CPAP value (9.15 +/- 0.47 cmH(2)O) was virtually the same as the pressure that optimized breathing during hospital polysomnography (9.20 +/- 0.41 cmH(2)O; mean difference: 0.02 cmH(2)O, limits of agreement: +/- 1.00 cmH(2)O). Conclusions: This pilot study shows that a simple telemetric system, requiring neither a special telemedicine network nor any infrastructure in the patient's home, made it possible to perform effective remote CPAP titration on SAHS patients.

JTD Keywords: Home CPAP titration by telemetry, Telecare, Telemedicine, E-health, Obstructive sleep apnea, Point of care