Miloš Ajčević

Characterization of the mechanical behavior of intrapulmonary percussive ventilation

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.

Impact of Aging on Heart Rate Variability Properties of Healthy Subjects

Heart Rate Variability (HRV) has been studied in a variety of clinical situations in order to quantify the modulations in the heart rate associated to different pathological conditions. Nevertheless, significant changes in spectral and some nonlinear parameters of the HRV were reported also in normal subjects, depending on age and gender. The aim of this work was to quantify the age-related differences in other nonlinear parameters, particularly in the fractal dimension, of the HRV of healthy subjects and to compare the results with the changes showed by spectral measures. The RR time series extracted by the Holter monitoring of 60 healthy subjects, divided into three groups similar for both age and gender, were accurately analyzed. The results only partially revealed age-related changes both in the spectral and fractal HRV measures, underlining the need to carefully examine the RR data selection and the pre-processing phases.

In vitro estimation of pressure drop across tracheal tubes during high-frequency percussive ventilation

Tracheal tubes (TT) are used in clinical practice to connect an artificial ventilator to the patients 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 Rohrers 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.

Tailoring of HFPV Treatment by Respiratory Parameters Measurement

High-frequency percussive ventilation (HFPV) is a non-conventional ventilatory modality which has proven highly effective in patients with severe gas exchange impairment. HFPV delivers a series of high-frequency sub-tidal volumes, by pulsatile flow, in combination with low-frequency breathing cycles. Currently, the mean airway pressure is the only parameter that allows an indirect comparison between conventional volume controlled ventilation and HFPV treatment set up. In the clinical practice, HFPV is not an intuitive ventilatory modality and the absence of valuable information during the treatment as delivered tidal volume, respiratory system resistance and compliance produces disaffection among the physicians. The aim of this study was to tailor HFPV treatment setup, avoiding under and overtreatment, using new bedside measurements of the main respiratory parameters. For this purpose pressure, flow and volume were measured in six patients undergoing HFPV. Respiratory system resistance and compliance were estimated using multiple linear regression method on Dorkin’s high frequency model, while endotracheal tube pressure drop was estimated using Blasius’s model.

Estimation of the difference between peak airway and tracheal pressures during HFPV

High frequency percussive ventilation (HFPV) is an advanced ventilatory strategy which has proven very effective in patients with acute respiratory failure. The airway pressure measured by HFPV ventilator represents the sum of the endotracheal tube pressure drop and the tracheal pressure dissipated to inflate a lung. The estimation of the difference between the peak airway and tracheal pressure APp may be very useful to the clinician to avoid lung injury. The aim of this study is to provide an in vitro estimation of APp based only on the ventilator set parameters (i.e. peak pressures, pulsatile frequencies) and the patients respiratory system resistance and compliance. The model for the estimation of APp was determined by using the Least Absolute Shrinkage and Selection Operator (LASSO) regularized least-squares regression technique. The identified model was successively assessed on test data set.

A novel estimation methodology for tracheal pressure in mechanical ventilation control

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.

A Mobile App for the Self-management of Type 1 Diabetes as Tool for Preventing of Exercise-Associated Glycemic Imbalances

mHealth is a growing field of research, concerning the great potentialities of mobile technology as a tool for self-management of chronic conditions. Physical activity greatly influences blood glucose levels, therefore for type 1 diabetes patients is important to adapt their diet and therapy in order to avoid exercise-induced hyperglycemia and hypoglycemia. The later represents one of the major barriers to physical activity and it limits volitional exercise in type 1 diabetes patients. However, there is lack of stand-alone mobile tool that provides the support to the patient in order to perform physical activity and exercise under safe glycaemia levels. Recently, Exercise Carbohydrate Requirement Estimating Software (ECRES) algorithm was proposed to calculate patient-exercise tailored glucose supplement required to maintain safe blood glucose levels during physical activity. The objective of this study was to develop a mobile App which implements an individualized predictive system for blood glucose in type 1 diabetes, depending on exercise strength. Its usability and accuracy were compared to original ECRES estimating software in 15 volunteer subjects. The developed application provides relevant feedback to patients on carbohydrate intake needed to carry out a planned physical activity, in a safe manner. Furthermore, application provides other important features, for self-management of this chronicity, reported in recent literature: entry of blood glucose values, display of diabetes-related data, such as blood glucose readings and their analysis, carbohydrate intake, insulin doses, and easy data export. The application also incorporates food atlas in order to facilitate carbohydrates calculation. The results of the test showed that developed application accurately implements ECRES algorithm and the self-management features. In conclusion, proposed App could be a useful support tool to diabetes type 1 patents. The results should be confirmed in larger clinical study.

Comparison of ECRES Algorithm with Classical Method in Management of Diabetes Type 1 Exercise-Related Imbalances

Nutrition and physical activity are important parts of a healthy lifestyle and management of diabetes. Regular moderate-intensity physical activity in type 1 diabetes patients can enhance insulin sensitivity, reduce the risk of cardiovascular disease and improve psychological well-being. Nevertheless, the risk of exercise-induced hypoglycemia is a great challenge for patients with type 1 diabetes and represents an important barrier to physical activity in these patients. Recently, an algorithm called ECRES has been developed with the aim of estimating, depending on patient’s own therapy and specific physical activity, the glucose supplement required by the patient to maintain safe blood glucose levels. The aim of this study is to compare the ECRES algorithm to classical quantitative approach. Therefore, we measured and compared glycaemia in 23 patients (mean age: 43 ± 12 years) during 1-h treadmill walk/run maintaining heart rate at 65% of his/her theoretical maximum value for age. For each subject two separate tests were performed: with carbohydrates supplement estimated by ECRES algorithm and by classical approach, respectively. The average heart rate observed during exercise (average progression speed: 5.8 ± 0.8 km/h at 4.2 ± 2.3% inclination) was 111.5 ± 9.4 bpm. Glycaemia measured by portable glucometer showed no significant differences between tests managed with ECRES algorithm and with classical approach, both before (149 ± 47 vs. 128 ± 41 mg/dL) and at the end of the performed exercise (134 ± 66 vs. 138 ± 54 mg/dL). The ECRES algorithm, however, estimated a significantly lower amount of carbohydrate needed for physical activity as compared to that suggested by the classical approach (14.8 ± 12.0 g vs. 23.4 ± 4.7 g; p < 0.05), while maintaining patients’ blood glucose within optimal clinical limits. The study results confirmed the validity of the estimates made by the ECRES algorithm.

Analysis of Kinematic Parameters Relationships in Normal and Dysgraphic Children

Kinematic analysis of handwriting, using digital tablet together with appropriate tests and processing software, allows evaluation of subject graphomotor abilities. Several studies focused on the kinematic parameters extracted from the writing of both normal subjects and subjects with specific disorder, such as dysgraphia, recorded during simple tasks. However, relationships between these parameters have not yet been analyzed. The aim of this study was to examine and identify possible links between kinematic parameters like Amplitude (SA), Duration (SD) and Peak Velocity (PkVS) of Strokes extracted from handwriting and their changes between normal and dysgraphic children. Fifty normal and eighteen dysgraphic children, attending classes from 2nd to 5th grade of primary school, were involved in the study; three cursive tests were administered and analyzed. Results showed a linear relation between SA and PkVS parameters with a similar slope in both groups and reduced peak velocities in some dysgraphic children. A linear relationship was also present between SA and SD parameters in all tests for normal children while an unclear relation was found for dysgraphic children. The latter showed longer durations and slightly lower amplitudes than in normal subjects, in all tasks, as expected in case of poor fluency.

Wake-up stroke and CT perfusion: effectiveness and safety of reperfusion therapy

ObjectiveIschemic stroke is a neuroemergency condition highly treatable with thrombolysis and thrombectomy. Recently, observational studies have brought insights into clinical and imaging characteristics of wake-up stroke, which interested up to 25% of ischemic stroke patients. In clinical practice, wake-up strokes are usually not considered for reperfusion therapy. The aim of this study was to investigate the use CT perfusion imaging in patients with wake-up stroke and to assess the effect of neuroimaging information provided by CT perfusion maps on the efficacy and safety of thrombolysis and thrombectomy.Patients and methodWe studied 22 wake-up stroke (WUS) patients (13F/9M mean age) who underwent reperfusion therapy after the eligibility assessed by the CT perfusion imaging (< 50% core-to-penumbra ratio and negative CT perfusion).ResultsMean National Institutes of Health Stroke Scale (NIHSS) was 8.1 ± 4.9 at admission while 3.3 ± 5.1 at discharge, significantly different from admission (p < 0.001). As many as ten patients had mRS lower than 3 at discharge. Intracranial hemorrhage occurred in five patients and caused symptoms worsening only in two patients (decrease of NIHSS score of 4 points) of which one patient died.ConclusionThe main finding of this study is that wake-up stroke with adequate selection by CT perfusion may benefit reperfusion treatment.