Genetic parameters for milk somatic cell score and relationships with production traits in primiparous dairy sheep

A total of 13,066 first-lactation test-day records of 2,277 Valle del Belice ewes from 17 flocks were used to estimate genetic parameters for somatic cell scores (SCS) and milk production traits, using a repeatability test-day animal model. Heritability estimates were low and ranged from 0.09 to 0.14 for milk, fat, and protein yields, and contents. For SCS, the heritability of 0.14 was relatively high. The repeatabilities were moderate and ranged from 0.29 to 0.47 for milk production traits. The repeatability for SCS was 0.36. Flock-test-day explained a large proportion of the variation for milk production traits, but it did not have a big effect on SCS. The genetic correlations of fat and protein yields with fat and protein percentages were positive and high,indicating a strong association between these traits. The genetic correlations of milk production traits with SCS were positive and ranged from 0.16 to 0.31. The results showed that SCS is a heritable trait in Valle del Belice sheep and that single-trait selection for increased milk production will also increase SCS

Deep analysis of EIT dataset to classify apnea and non-apnea cases in neonatal patients

Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates' EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features

PMU31 early cost-effectiveness analysis of continuous monitoring of lung-aeration with electrical impedance tomography in preterm neonates with respiratory distress syndrome

Objectives Respiratory distress syndrome (RDS) is relatively common in preterm neonates due to lung immaturity. Clinical management by respiratory support is associated with high complications rates. Guidance on appropriate lung-aeration is limited using conventional thorax X-ray monitoring. Electrical impedance tomography (EIT) allows radiation-free, continuous lung-aeration monitoring to guide effective respiratory support. EIT produces dynamic images of air volume changes whereas X-ray shows 2-D structure. Clinicians expect EIT implementation to reduce the number of patients requiring mechanical ventilation, overall complication rates and hospitalisation length. We conducted an early cost-effectiveness analysis of EIT-monitoring in preterm neonates with RDS versus standard care in the Netherlands. Methods A decision-analytic model was constructed comparing costs and effects of conventional X-ray versus EIT-monitoring for preterm neonates with RDS from the healthcare perspective with a time horizon of two years. Input parameters were based on literature and cost databases. The effects of EIT-monitoring were based on consensus by 6 clinical experts for two scenarios, (1) a conservative scenario assuming only a decrease of patients on mechanical ventilation under EIT-monitoring, and (2) an optimistic scenario including scenario (1) and assuming an additional 10% relative complication rate decrease in comparison to standard care. Main outcomes were total average costs per patient, number of patients with bronchopulmonary dysplasia (BPD), and mortality. One-way sensitivity analyses were conducted. Results EIT-monitoring was estimated to be cost-saving in both scenarios, mainly due to a shorter average hospital length of stay. Total incremental costs per patient for EIT-monitored care versus standard care were -€929 and -€10,706 for scenario (1) and (2), respectively. The number of patients with BPD and deaths were reduced. Results were robust to changes in input parameters. Conclusions EIT lung-aeration monitoring in preterm neonates is expected to result in cost-savings and lower mortality and BPD rates, in comparison to standard care, in a Dutch hospital setting. Copyright © 2019 Published by Elsevier Inc

Electrical impedance tomography reveals pathophysiology of neonatal pneumothorax during NAVA

Pneumothorax is a potentially life‐threatening complication of neonatal respiratory distress syndrome (RDS). We describe a case of a tension pneumothorax that occurred during neurally adjusted ventilatory assist (NAVA) in a preterm infant suffering from RDS. The infant was included in a multicenter study examining the role of electrical impedance tomography (EIT) in intensive care and therefore continuously monitored with this imaging method. The attending physicians were blinded for EIT findings but offline analysis revealed the potential of EIT to clarify the underlying cause of this complication, which in this case was heterogeneous lung disease resulting in uneven ventilation distribution. Instantaneous increase in end‐expiratory lung impedance on the affected side was observed at time of the air leak. Real‐time bedside availability of EIT data could have modified the treatment decisions made

Deep Analysis of EIT Dataset to Classify Apnea and Non-apnea Cases in Neonatal Patients

Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates’ EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features

Regional respiratory time constants during lung recruitment in high-frequency oscillatory ventilated preterm infants

To assess the regional respiratory time constants of lung volume changes during stepwise lung recruitment before and after surfactant treatment in high-frequency oscillatory ventilated preterm infants. A stepwise oxygenation-guided recruitment procedure was performed before and after surfactant treatment in high-frequency oscillatory ventilated preterm infants. Electrical impedance tomography was used to continuously record changes in lung volume during the recruitment maneuver. Time constants were determined for all incremental and decremental pressure steps, using one-phase exponential decay curve fitting. Data were analyzed for the whole cross section of the chest and the ventral and dorsal lung regions separately. Before surfactant treatment, the time constants of the incremental pressure steps were significantly longer (median 27.3 s) than those in the decremental steps (16.1 s). Regional analysis showed only small differences between the ventral and dorsal lung regions. Following surfactant treatment, the time constants during decremental pressure steps almost tripled to 44.3 s. Furthermore, the time constants became significantly (p <0.01) longer in the dorsal (61.2 s) than into the ventral (40.3 s) lung region. Lung volume stabilization during stepwise oxygenation-guided lung recruitment in high-frequency oscillatory ventilated preterm infants with respiratory distress syndrome is usually completed within 5 min and is dependent on the position of ventilation on the pressure volume curve, the surfactant status, and the region of interest of the lun

Compressive sensing in electrical impedance tomography for breathing monitoring

Continuous functional thorax monitoring using EIT has been extensively researched. A limiting factor in high temporal resolution, three dimensional, and fast EIT is the handling of the volume of raw impedance data produced for transmission and storage. Owing to the periodicity of breathing that may be reflected in EIT boundary measurements, data dimensionality may be reduced efficiently at the time of sampling using compressed sensing techniques. Measurements using a 32-electrode 48-frame-per-second EIT system from 30 neonates were post-processed to simulate random demodulation acquisition method on 2000 frames for compression ratios (CRs) ranging from 2-100. Sparse reconstruction was performed by solving the basis pursuit problem using SPGL1 package. The global impedance data was used in the subsequent studies. The signal to noise ratio (SNR) for the entire frequency band (0 Hz - 24 Hz) and three local frequency bands were analysed. A breath detection algorithm was applied to traces and the subsequent error-rates were calculated while considering the outcome of the algorithm applied to a down-sampled and linearly interpolated version of the traces as the baseline. SNR degradation was proportional with CR. The mean degradation for 0 Hz - 8 Hz was below ~15 dB for all CRs. The error-rates in the outcome of the breath detection algorithm in the case of decompressed traces were lower than those of the associated down-sampled traces for CR≥25, corresponding to sub-Nyquist rate for breathing. For instance, the mean error-rate associated with CR = 50 was ~60% lower than that of the corresponding down-sampled traces. To the best of our knowledge, no other study has evaluated compressive sensing on boundary impedance data in EIT. While further research should be directed at optimising the acquisition and decompression techniques for this application, this contribution serves as the baseline for future efforts. [Abstract copyright: Creative Commons Attribution license.

Effect of routine suction on lung aeration in critically ill neonates and young infants measured with electrical impedance tomography

oai:repository.mdx.ac.uk:y1752Endotracheal suctioning is a widely used procedure to remove secretions from the airways of ventilated patients. Despite its prevalence, regional effects of this maneuver have seldom been studied. In this study, we explore its effects on regional lung aeration in neonates and young infants using electrical impedance tomography (EIT) as part of the large EU-funded multicenter observational study CRADL. 200 neonates and young infants in intensive care units were monitored with EIT for up to 72 h. EIT parameters were calculated to detect changes in ventilation distribution, ventilation inhomogeneity and ventilation quantity on a breath-by-breath level 5–10 min before and after suctioning. The intratidal change in aeration over time was investigated by means of regional expiratory time constants calculated from all respiratory cycles using an innovative procedure and visualized by 2D maps of the thoracic cross-section. 344 tracheal suctioning events from 51 patients could be analyzed. They showed no or very small changes of EIT parameters, with a dorsal shift of the center of ventilation by 0.5% of the chest diameter and a 7% decrease of tidal impedance variation after suctioning. Regional time constants did not change significantly. Routine suctioning led to EIT- detectable but merely small changes of the ventilation distribution in this study population. While still a measure requiring further study, the time constant maps may help clinicians interpret ventilationmechanics in specific cases

Optimized breath detection algorithm in electrical impedance tomography

This paper defines a method for optimizing the breath delineation algorithms used in Electrical Impedance Tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern. Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project. Three different algorithms are proposed that are improving the breath detector performance by adding conditions on 1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, 2) minimum tidal impedance amplitude and 3) minimum tidal breath rate obtained from Time-Frequency (TF) analysis. As a baseline the zero crossing algorithm has been used with some default parameters based on the Swisstom EIT device. Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the Receiver Operating Characterics (ROC). This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors. [Abstract copyright: © 2018 Institute of Physics and Engineering in Medicine.