The overarching objectives of CRADL are:
1) To deliver a tool that provides continuous, non-invasive, radiation free, bedside information on regional lung aeration and ventilation during daily clinical care of (preterm) infants and children with respiratory failure.
2) To assess the effectiveness, efficacy and safety of such a system in guiding respiratory management and supportive care of the most common causes of paediatric respiratory failure (respiratory distress syndrome,
bronchiolitis and acute respiratory distress syndrome), with the final goal to reduce short and long term adverse effects of disease and its treatment in this population.
key results:
1) The multicentre clinical CRADL study accomplished within WP1 generated the largest database of clinical EIT data in the world equalling to approximately 1.9 billion of primary EIT images (about 437 days of data
acquired at a sampling rate of 49 images/s)
2) With 200 patients included, the CRADL study is one of the largest clinical studies ever carried out.
3) The CRADL study is the first clinical study using EIT technology where study participants were examined during routine clinical care. No study-related interventions were performed meaning that the current results
reflect for the first time the unbiased potential benefit of EIT monitoring in critically ill neonatal and paediatric patients.
4) In spite of the fact that the examinations were conducted during routine care, we could demonstrate that skin contact impedance, essential for reliable generation of EIT images, was sufficient during 85% of individual
examination times.
5) In contrast to all earlier EIT studies, in which the patients were typically examined by EIT only during short periods of time (below one hour), the CRADL study participants were continuously monitored by EIT for up to
three days.
6) Although EIT is generally accepted to be a non-invasive and non-harmful medical technology and no adverse events associated with this technology were described in previous studies performed in neonates, infants and
children only the CRADL study provided the evidence that the use of EIT is safe in this very fragile patient population even during long-term monitoring.
7) The data generated in WP1 provided important patient case data that were implemented as case scenarios during the development and testing of the neonatal/paediatric Graphical User Interface developed in WP2.
8) The clinical CRADL EIT data was essential for the progress in WP5 where it was used for instance in the development of the breath detection algorithm. Since the breathing pattern of preterm and term neonates is
extremely variable, regarding the respiratory rate, tidal volume and the incidence of apnoeas, the newly developed evaluation algorithms were essential for the successful completion of the CRADL data analyses. These
novel breath-detection algorithms are highly relevant for the future clinical use of EIT in neonatal patients.
9) The EIT data acquired in the CRADL study was also utilized in the development of other EIT evaluation algorithms (which are described within WP4), aiming at the assessment of regional lung ventilation behaviour.
10) The patient examinations conducted during the CRADL study generated important inputs for further optimisation of the technology, leading for instance to a new design of the EIT electrode belts for neonates.
11) EIT was shown to detect changes in ventilation homogeneity in response to a variety of nursing and medical interventions in the CRADL study, like posture change, suctioning, administration of surfactant, change in
invasive to non-invasive ventilation (and vice versa), etc. The main EIT measures calculated to analyse these changes in ventilation distribution were the global inhomogeneity index, coefficient of variation and the
centre of ventilation.
12) The CRADL clinical study could also demonstrate the capacity of EIT to detect adverse events, like pneumothorax, atelectasis, endotracheal tube misplacement and pleural effusion.
13) Design of a Graphical User Interface (GUI), and informed WP4, with respect to the data processing algorithms they were developing.
14) Novel integrated circuits in order to improve the performance of the wearable EIT system in terms of higher operating bandwidth, multi-frequency operation, improved signal to noise ratio, lower common-mode
errors, lower power consumption, and incorporation of sensors to measure the boundary curvature.
15) Develop new imaging software and algorithms that could assist making clinical diagnoses and treatment decisions in neonatal lung EIT.
16) New breath-detection algorithm has facilitated the evaluation of the various performance measures that has been studied.
17) A detection of lung ventilation based on a polynomial fit of regional vs. global impedance changes during breathing.
18) A mathematical modelling of the changes in the complex valued conductivity of a lung
19) Compressive sensing in electrical impedance tomography for breathing monitoring.
20) New robust forward modelling in lung EIT.
Conclusions on the project
Clinicians expect EIT monitoring to better inform decisions on ventilation management and - as a consequence - to reduce the number of patients requiring mechanical ventilation, overall complication rates and hospitalisation length. EIT monitoring was estimated to be cost-saving in, mainly due to a shorter average hospitalisation length. CRADL has brought the adoption of EIT a major step closer.
