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Intubation and mechanical ventilation are frequently used in case of impaired gas exchange. High-frequency percussive ventilation (HFPV) is a nonconventional ventilation modality, which has been proven highly useful in the treatment of several different pathological pulmonary conditions. The pressure measured by the ventilator represents the sum of the tracheal pressure and of the pressure dissipated by endotracheal tube. Given that HFPV is not an intuitive ventilatory modality, the possibility to know the real value of tracheal pressure will be very helpful to optimize the treatment. This study compares two different methods for the estimation of the tracheal pressure in order to ascertain the applicability and limitations of these approaches in the clinical practice. The two models are based on fluid dynamics and on genetic programing. The simple former model for estimation of tracheal pressure showed to be effective and easily applied in everyday clinical practice.

Intrapulmonary percussive ventilation (IPV) is an airway clearance technique recently introduced as an alternative to conventional chest physiotherapy in chronic obstructive pulmonary diseases (COPD). A new portable device delivering IPV, called Impulsator® (Percussionaire Corporation, Sandpoint, Idaho, USA), has been introduced for IPV home therapy with the aim to foster COPD patients’ autonomy. Nevertheless, the lack of detailed information about the exact functioning of the device restricts the possibility of treatment optimization, possibly reducing the clinical usefulness of the therapy. This work aims to obtain quantitative information about the physical variables related to the use of the Impulsator® (e.g. pulsatile flow and pressure magnitudes), allowing treatment personalization.

High-frequency percussive ventilation (HFPV) is a ventilation modality which has been proved useful as an alternative to conventional mechanical ventilation. In clinical practice the ventilator measures the pressure that represents the sum of the pressure drop due to the endotracheal tube (ΔPEET) and the pressure dissipated to inflate lung. From the clinical point of view, it is of paramount importance to estimate the real amount of ΔPEET. This study aimed at identifying in vitro the most adequate model and parameters for estimating ΔPEET of pediatric endotracheal tube during HFPV, under different working pressures, percussive frequencies and resistive and elastic lung loads. The results show that it is possible to estimate ΔPEET in pediatric endotracheal tubes by using a simple Blasius’ model, considering the presence of inertance. The Blasius’ model presents the same estimation error as Rohrer’s model and its coefficients result largely independent from ventilator settings and lung loads.

There is a growing research interest in wireless non-invasive solutions for core temperature estimation and their application in clinical settings. This study aimed to investigate the use of a novel wireless non-invasive heat flux-based thermometer in acute stroke patients admitted to a stroke unit and compare the measurements with the currently used infrared (IR) tympanic temperature readings. The study encompassed 30 acute ischemic stroke patients who underwent continuous measurement (Tcore) with the novel wearable non-invasive CORE device. Paired measurements of Tcore and tympanic temperature (Ttym) by using a standard IR-device were performed 3–5 times/day, yielding a total of 305 measurements. The predicted core temperatures (Tcore) were significantly correlated with Ttym (r = 0.89, p < 0.001). The comparison of the Tcore and Ttym measurements by Bland–Altman analysis showed a good agreement between them, with a low mean difference of 0.11 ± 0.34 °C, and no proportional bias was observed (B = −0.003, p = 0.923). The Tcore measurements correctly predicted the presence or absence of Ttym hyperthermia or fever in 94.1% and 97.4% of cases, respectively. Temperature monitoring with a novel wireless non-invasive heat flux-based thermometer could be a reliable alternative to the Ttym method for assessing core temperature in acute ischemic stroke patients.

Motor-Imagery based BCI (MI-BCI) neurorehabilitation can improve locomotor ability and reduce the deficit symptoms in Parkinson's Disease patients. Advanced Motor-Imagery BCI methods are needed to overcome the accuracy and time-related MI BCI calibration challenges in such patients. In this study, we proposed a Multi-session FBCSP (msFBCSP) based on inter-session transfer learning and we investigated its performance compared to the single-session based FBSCP. The main result of this study is the significantly improved accuracy obtained by proposed msFBCSP compared to single-session FBCSP in PD patients (median 81.3%, range 41.2-100.0% vs median 61.1%, range 25.0-100.0%, respectively; p

The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients’ clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from HRV, a signal easily obtained from the ECG. In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects (156 normal subjects and 87 IHD patients) were used to train and validate a series of several ANN, different for number of input and hidden nodes. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset, respectively.

Blood Pressure (BP) and Heart Rate (HR) provide information on clin-ical condition along 24h. Both signals present circadian changes due to sympa-thetic/parasympathetic control system that influence the relationship between them. Moreover, also the gender could modify this relation, acting on both con-trol systems. Some studies, using office measurements examined the BP/HR re-lation, highlighting a direct association between the two variables, linked to sus-pected coronary heart disease. Nevertheless, till now such relation has not been studied yet using ambulatory technique that is known to lead to additional prog-nostic information about cardiovascular risks. In order to examine in a more ac-curate way this relation, in this work we evaluate the influence of gender on the BP/HR relationship by using hour-to-hour 24h ambulatory measurements. Data coming from 122 female and 50 male normotensive subjects were recorded using a Holter Blood Pressure Monitor and the parameters of the linear regression fit-ting BP/HR were calculated. Results confirmed those obtained in previous stud-ies using punctual office measures in males and underlined a significant relation between Diastolic BP and HR during each hour of the day in females; a different trend in the BP/HR relation between genders was found only during night-time. Moreover, the circadian rhythm of BP/HR is similar in both genders but with different values of HR and BP at different times of the day.

SummarySolutions for improving management of chronic conditions are under the attention of healthcare systems, due to the increasing prevalence caused by demographic change and better survival, and the relevant impact on healthcare expenditures. The objective of this study was to propose a comprehensive architecture of a mHealth system aimed at boosting the active and informed participation of patients in their care process, while at the same time overcoming the current technical and psychological/clinical issues highlighted by the existing literature.After having studied the current challenges outlined in the literature, both in terms of technological and human requirements, we focused our attention on some specific psychological aspects with a view to providing patients with a comprehensive and personalized solution. Our approach has been reinforced through the results of a preliminary assessment we conducted on 22 patients with chronic conditions. The main goal of such an assessment was to provide a preliminary understanding of their needs in a real context, both in terms of self-awareness and of their predisposition toward the use of IT solutions.According to the specific needs and features, such as mindfulness and gamification, which were identified through the literature and the preliminary assessment, we designed a comprehensive open architecture able to provide a tailor-made solution linked to specific individuals’ needs.The present study represents the preliminary step towards the development of a solution aimed at enhancing patients’ actual perception and encouraging self-management and selfawareness for a better lifestyle. Future work regards further identification of pathology-related needs and requirements through focus groups including all stakeholders in order to describe the architecture and functionality in greater detail.Citation: Lasorsa I, D’Antrassi P, Ajčević M, Stellato K, Di Lenarda A, Marceglia S, Accardo A. Personalized support for chronic conditions: a novel approach for enhancing self-management and improving lifestyle

Direct and indirect economic costs related to chronic diseases are increasing in Europe due to the aging of population. One of the most challenging goals is to improve the quality of life of patients affected by chronic conditions, and enhance their self-management. In this paper, we propose a novel architecture of a scalable solution, based on mobile tools, aimed to keep patients with chronic diseases away from acute episodes, to improve their quality of life and, consequently, to reduce their economic impact. Our solution aims to provide patients with a personalized tool for improving self-management, and it supports both patients and clinicians in decision-making through the implementation of two different Decision Support Systems. Moreover, the proposed architecture takes into account the interoperability and, particularly, the compliance with data transfer protocols (e.g., BT4/LE, ANT+, ISO/IEEE 11073) to ensure integration with existing devices, and with the semantic web approaches and standards related to the content and structure of the information (e.g., HL7, ICD-10 and openEHR) to ensure correct sharing of information with hospital information systems, and classification of patient behaviors (Coelition). The solution will be implemented and validated in future study.

Tracheal tubes (TT) are used in clinical practice to connect an artificial ventilator to the patient's airways. It is important to know the pressure used to overcome tube impedance to avoid lung injury. Although high-frequency percussive ventilation (HFPV) has been increasingly used, the mechanical behavior of TT under HFPV has not yet been described. Thus, we aimed at characterizing in vitro the pressure drop across TT (ΔPTT) by identifying the model that best fits the measured pressure-flow (P-V̇) relationships during HFPV under different working pressures (PWork), percussive frequencies and mechanical loads. Three simple models relating ΔPTT and flow (V̇) were tested. Model 1 is characterized by linear resistive [Rtube ⋅ V̇(t)] and inertial [I · V̈(t)] terms. Model 2 takes into consideration Rohrer's approach [K1· V̇(t) + K2 ⋅V̇(t)] and inertance [I ·V̈(t)]. In model 3 the pressure drop caused by friction is represented by the non-linear Blasius component [Kb· V̇(1.75)(t)] and the inertial term [I· V̈(t)]. Model 1 presented a significantly higher root mean square error of approximation than models 2 and 3, which were similar. Thus, model 1 was not as accurate as the latter, possibly due to turbulence. Model 3 presented the most robust resistance-related coefficient. Estimated inertances did not vary among the models using the same tube. In conclusion, in HFPV ΔPTT can be easily calculated by the physician using model 3.

The respiratory mechanics parameters (resistance and elastance) are important for adequate mechanical ventilation settings and for a correct clinical diagnosis. High-frequency percussive ventilation (HFPV) is a non-conventional ventilatory strategy characterized by inspiratory high frequency pulsatile flow, while the expiratory phase is passive. It associates the beneficial aspects of conventional mechanical ventilation with those of high-frequency ventilation. This bench study, as preliminary to patients study, aimed to estimate respiratory mechanics parameters through a non-invasive method, during HFPV at different working pressures, frequencies and mechanical (resistive and elastic) loads. For such purpose, a multiple linear regression method to estimate parameters of the Dorkin high frequency pulmonary model was applied, starting from in vitro measured pressure and flow signals, considering inspiratory phase exclusively. The results encourage further in vivo studies of this methodology, which has to be clinically assessed. The low rmse% values confirm validity of Dorkin three element model during HFPV.

Introduction. High-frequency percussive ventilation (HFPV) is an unconventional ventilatory strategy that associates the beneficial aspects of conventional mechanical ventilation (CMV) with those of high-frequency ventilation (HFV). Accurate measurement of respiratory mechanics performed on intubated patients requires to take into account the real amount of pressure dissipated by endotracheal tube (EET). The purpose of this preliminary study was to characterize the endotracheal tube pressure drop (ΔP) during HFPV at different working pressures, frequencies and mechanical loads.

A new device delivering intrapulmonary percussive ventilation (IPV), called Impulsator® (Percussionaire Corporation, Sandpoint, ID, USA), has recently been introduced in an effort to provide effective clearance and to promote homogeneity of ventilation in the lungs of patients with cystic fibrosis. In order to optimize the treatment based on its use, a better understanding of its functioning is still necessary. In fact, up to now, a complete characterization of this device has not been carried out, thus reducing its effective utilization in clinical practice. With the aim of overcoming this lack, in this study, data concerning flow and pressure delivered during in vitro IPV were acquired under different combinations of device settings and respiratory loads. Quantitative information was obtained about the physical variables administered by the device like percussive frequency, ratio of inspiratory to expiratory time, flow and pressure magnitudes and volume exchanged. The analysis of the data determined the relations among these variables and between them and the mechanical loads, laying the basis for an optimal clinical application of the device.

High-frequency percussive ventilation (HFPV) is a non-conventional mechanical ventilatory strategy which has proven useful in the treatment of a number of pathological conditions. HFPV usually involves the usage of endotracheal tubes (EET) connecting the ventilator circuit to the airway of the patient. The pressure of the air flow insufflated by HFPV must be controlled very accurately in order to avoid barotrauma and volutrauma. Since the actual tracheal pressure cannot be measured, a model for estimating such a pressure based on the EET properties and on the air flow properties that can actually be measured in clinical practice is necessary. In this work we propose a novel methodology, based on Genetic Programming, for synthesizing such a model. We experimentally evaluated our models against the state-of-the-art baseline models, crafted by human experts, and found that our models for estimating tracheal pressure are significantly more accurate.