Telemonitoring in Chronic Obstructive Pulmonary Disease (CHROMED). A Randomized Clinical Trial.

RATIONALE: Early detection of chronic obstructive pulmonary disease (COPD) exacerbations using telemonitoring of physiological variables might reduce the frequency of hospitalization. OBJECTIVES: To evaluate the efficacy of home monitoring of lung mechanics by the forced oscillation technique and cardiac parameters in older patients with COPD and comorbidities. METHODS: This multicenter, randomized clinical trial recruited 312 patients with Global Initiative for Chronic Obstructive Lung Disease grades II to IV COPD (median age, 71 yr [interquartile range, 66-76 yr]; 49.6% grade II, 50.4% grades III-IV), with a history of exacerbation in the previous year and at least one nonpulmonary comorbidity. Patients were randomized to usual care (n = 158) or telemonitoring (n = 154) and followed for 9 months. All telemonitoring patients self-assessed lung mechanics daily, and in a subgroup with congestive heart failure (n = 37) cardiac parameters were also monitored. An algorithm identified deterioration, triggering a telephone contact to determine appropriate interventions. MEASUREMENTS AND MAIN RESULTS: Primary outcomes were time to first hospitalization (TTFH) and change in the EuroQoL EQ-5D utility index score. Secondary outcomes included: rate of antibiotic/corticosteroid prescription; hospitalization; the COPD Assessment Tool, Patient Health Questionnaire-9, and Minnesota Living with Heart Failure questionnaire scores; quality-adjusted life years; and healthcare costs. Telemonitoring did not affect TTFH, EQ-5D utility index score, antibiotic prescriptions, hospitalization rate, or questionnaire scores. In an exploratory analysis, telemedicine was associated with fewer repeat hospitalizations (-54%; P = 0.017). CONCLUSIONS: In older patients with COPD and comorbidities, remote monitoring of lung function by forced oscillation technique and cardiac parameters did not change TTFH and EQ-5D. Clinical trial registered with www.clinicaltrials.gov (NCT 01960907).

Presepsin for the detection of early-onset sepsis in preterm newborns.

BACKGROUND: Early-onset sepsis (EOS) is responsible for an important fraction of neonatal morbidity and mortality all over the world. The aim of this study was to assess whether presepsin (P-SEP) can be a more accurate biomarker of EOS compared with pro-calcitonin (PCT) and C-reactive protein (CRP). STUDY DESIGN: Consecutive preterm neonates (<34 wk gestational age, admitted to Neonatal Intensive Care Unit by 6 h of age and undergoing sepsis evaluation) were recruited as part of a case-matched control study. We determined CRP, PCT and P-SEP at admission, and then at 12, 24, and 48 h of age. Neonates recruited into the study were divided into the EOS group (n = 32) and the uninfected group (n =38) according to their infection screening. RESULTS: P-SEP values were significantly higher in the EOS group than in the uninfected group at different time intervals. The highest accuracy was achieved by P-SEP at 24 h after birth. The AUC for P-SEP was 0.97. In our sample, P-SEP achieved the best accuracy for prediction of EOS at the cut-off of 788 ng/l with 93% sensitivity and 100% specificity. CONCLUSIONS: This study shows that P-SEP is significantly higher in preterm infants with EOS compared with uninfected infants.

False-positive troponin I elevation in a newborn with neonatal encephalopathy.

Serum biomarkers of myocardial damage are commonly used in babies after perinatal asphyxia. We present a case report of a persistently troponin I elevation without evidence of clinical or instrumental signs of myocardial ischaemia in a baby with perinatal asphyxia. When the blood was mixed with polyethylene glycol we found that the troponin I levels were falsely elevated due to interfering antibodies. This case shows that analytical errors may still occur despite modern immunoassay systems and underlines the need for further investigations to identify false-positive values in case of disagreement between clinical conditions and laboratory values.

Electrocardiographic and echocardiographic changes during therapeutic hypothermia in encephalopathic infants with long-term adverse outcome.

AIM: To assess the electrocardiography and echocardiography changes during therapeutic hypothermia and rewarming period in encephalopathic infants with long-term adverse neurological outcome. METHODS: Prospective multicentre longitudinal study. We included 64 consecutive infants with moderate or severe hypoxic ischaemic encephalopathy undergoing therapeutic hypothermia who had 18-24 month-outcome data. We analysed electrocardiography and heart rate changes before, during and after therapeutic hypothermia. Superior vena cava flow, left ventricular cardiac output and stroke volume were studied using echocardiography during and immediately after therapeutic hypothermia. An abnormal outcome was defined as death or moderate/severe disability at 18-24 months. RESULTS: Neonates with higher superior vena cava flow pre-rewarming had significantly higher odds of documented long-term adverse outcome when compared to newborns with good outcome (OR 1.57; 95%CI, 1.1-1.78; p = 0.01 after adjustment). QTc and RR intervals were significantly longer at 12, 24, 36 and 48 h in infants with good outcome compared with those with adverse outcome (p < 0.001). During therapeutic hypothermia, infants with poor outcome had a higher heart rate at 12, 24, 36, 48, 60 h after birth compared with those with good outcome (p < 0.001). From 36 h on, heart rate gradually increased and RR and QTc intervals progressively shortened with values back to normal after rewarming. CONCLUSIONS: Infants with hypoxic ischaemic encephalopathy who have adverse neurological outcome show a preferential cerebral blood flow redistribution during therapeutic hypothermia. Infants with poor outcome have higher heart rate and shorter RR and QTc intervals during therapeutic hypothermia.

A Robotic Solution for Assisting People with MCI at Home: Preliminary Tests of the ENRICHME System.

Robots have the potential to support care and independence of older adults. The ENRICHME project is developing an integrated system composed of a robot, sensors and a networking care platform, aiming at assisting older adults with MCI in their home environment. This paper reports findings of the tests performed on a sample of MCI users and their caregivers, with the first version of the ENRICHME system, in a controlled environment.

Reducing Fall Risk with Combined Motor and Cognitive Training in Elderly Fallers.

BACKGROUND: Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL. METHODS: In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an i-Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition). RESULTS: Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, p = 0.009) although to a limited extent (ES -0.25) restricted to the period after intervention. CONCLUSIONS: This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.

Identification of COPD patients' health status using an intelligent system in the CHRONIOUS wearable platform.

The CHRONIOUS system offers an integrated platform aiming at the effective management and real-time assessment of the health status of the patient suffering from chronic obstructive pulmonary disease (COPD). An intelligent system is developed for the analysis and the real-time evaluation of patient's condition. A hybrid classifier has been implemented on a personal digital assistant, combining a support vector machine, a random forest, and a rule-based system to provide a more advanced categorization scheme for the early and in real-time characterization of a COPD episode. This is followed by a severity estimation algorithm which classifies the identified pathological situation in different levels and triggers an alerting mechanism to provide an informative and instructive message/advice to the patient and the clinical supervisor. The system has been validated using data collected from 30 patients that have been annotated by experts indicating 1) the severity level of the current patient's health status, and 2) the COPD disease level of the recruited patients according to the GOLD guidelines. The achieved characterization accuracy has been found 94%.

Clinical validation of the CHRONIOUS wearable system in patients with chronic disease.

The CHRONIOUS system defines a powerful and easy to use framework which has been designed to provide services to clinicians and their patients suffering from chronic diseases. The system is composed of a wearable shirt that integrate several body sensors, a portable smart device and a central sub-system that is responsible for the long term storage of the collected patient's data. A multi-parametric expert system is developed for the analysis of the collected data using intelligent algorithms and complex techniques. Apart for the vital signals, dietary habits, drug intake, activity data, environmental and biochemical parameters are recorded. The CHRONIOUS platform is validated through clinical trials in several medical centers and patient's home environments recruiting patients suffering from Chronic Obstructive pulmonary disease (COPD) and Chronic Kidney Disease (CKD) diseases. The clinical trials contribute in improving the system's accuracy, while Pulmonologists and Nephrologists experts utilized the CHRONIOUS platform to evaluate its efficiency and performance. The results of the utilization of the system were very encouraging. The CHRONIOUS system has been proven to be a well-validated real-time patient monitoring and supervision platform, providing a useful tool for the clinician and the patient that would contribute to the more effective management of chronic diseases.

Pediatric reference values for urine particle quantification by using automated flow cytometer: results of a multicenter study of Italian urinalysis group.

OBJECTIVES: The purpose of this Italian multicenter study was to define pediatric upper reference values for urine particle quantification by using automated flow cytometry. DESIGN AND METHODS: Four hospital-based clinical laboratories participated in this multicenter investigation, which included a total study population of 161 Italian children aged from 1 to 12years. Two laboratories used Sysmex UF-100 and analyzed 86 children, whereas the other two used Sysmex UF-1000i and analyzed 75 subjects. Particle quantification included the analysis of white blood cells (WBC), red blood cells (RBC), squamous epithelial cells (EC), transitional epithelial cells (TC), casts (CAST) and bacteria (BACT). RESULTS: The upper reference values in subjects tested with the Sysmex UF-100 were 9.7WBC/µL, 10.1RBC/µL, 7.5EC/µL, 2.5TC/µL, 0.7CAST/µL and 3090BACT/µL, whereas the upper reference values in subjects tested with the Sysmex UF-1000i were 10.5WBC/µL, 8.3RBC/µL, 7.2EC/µL, 2.9TC/µL, 0.7CAST/µL and 48BACT/µL. No statistically significant differences between genders were found in the value distribution of any of the parameters tested. Similarly, no statistically significant differences were observed between the two urine analyzers, except for BACT. CONCLUSIONS: Automated analysis of urine particles appears a suitable means to optimize the workflow of routine urinalysis of children specimens. The upper reference limits for pediatric subjects obtained in this study were comparable to those previously reported in the literature, with no significant differences between genders and analyzers.

Categorization of COPD patient's health level through the use of the CHRONIOUS wearable platform.

The Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease that causes airflow blockage and breathing-related problems. As a Chronic disease it requires specific treatment plan and patient management for a long period of time. Critical factor in the process is the realization of frequent and precise diagnostic tests that describes the health status of the patient. The CHRONIOUS system provides the required easy-to-use wearable platform aiming at the successful management of COPD patients. Several signals and patient's data are stored by the utilization of an ergonomic jacket and through the patient's platform interface. Hybrid techniques based on supervised and unsupervised methodologies were applied for the analysis of the patient's situation. The categorization of health level of the patient to discrete levels is achieved in a continuous base. Useful outcomes in the form of message or advice are extracted appeared on patient's and clinician's devices denoting his health status.

Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management.

CHRONIOUS system is an integrated platform aiming at the management of chronic disease patients. One of the most important components of the system is a Decision Support System (DSS) that has been developed in a Smart Device (SD). This component decides on patient's current health status by combining several data, which are acquired either by wearable sensors or manually inputted by the patient or retrieved from the specific database. In case no abnormal situation has been tracked, the DSS takes no action and remains deactivated until next abnormal situation pack of data are being acquired or next scheduled data being transmitted. The DSS that has been implemented is an integrated classification system with two parallel classifiers, combining an expert system (rule-based system) and a supervised classifier, such as Support Vector Machines (SVM), Random Forests, artificial Neural Networks (aNN like the Multi-Layer Perceptron), Decision Trees and Naïve Bayes. The above categorized system is useful for providing critical information about the health status of the patient.

CHRONIOUS: a wearable platform for monitoring and management of patients with chronic disease.

The CHRONIOUS system has been developed based on an open architecture design that consists of a set of subsystems which interact in order to provide all the needed services to the chronic disease patients. An advanced multi-parametric expert system is being implemented that fuses information effectively from various sources using intelligent techniques. Data are collected by sensors of a body network controlling vital signals while additional tools record dietary habits and plans, drug intake, environmental and biochemical parameters and activity data. The CHRONIOUS platform provides guidelines and standards for the future generations of "chronic disease management systems" and facilitates sophisticated monitoring tools. In addition, an ontological information retrieval system is being delivered satisfying the necessities for up-to-date clinical information of Chronic Obstructive pulmonary disease (COPD) and Chronic Kidney Disease (CKD). Moreover, support tools are being embedded in the system, such as the Mental Tools for the monitoring of patient mental health status. The integrated platform provides real-time patient monitoring and supervision, both indoors and outdoors and represents a generic platform for the management of various chronic diseases.

An intelligent system for classification of patients suffering from chronic diseases.

The CHRONIOUS system addresses a smart wearable platform, based on multi-parametric sensor data processing, for monitoring people suffering from chronic diseases in long-stay setting. Several signals are being recorded through wearable sensors and are stored together with additional information, entered by the patient. An Intelligent System, placed at a Smart Assistant Device, analyzes incoming data and facilitates data mining techniques resulting upon the severity of a health episode. Part of the Intelligent System is the Mental Support Tool, which calculates a Stress Index and classifies the mental condition and stress levels of the patient. An additional component aiming at the personalization of the Intelligent System's Decision is the Profiler which defines several patients profiles and facilitates clustering techniques in order to associate each patient's description with one of the predefined profiles.