Glucocorticoid-induced hyperglycaemia in respiratory disease: a systematic review and meta-analysis.

The relative risk of glucocorticoid-induced hyperglycaemia is poorly quantified. We undertook a meta-analysis to estimate the association between glucocorticoid treatment and hyperglycaemia, overall and separately in individuals with and without diabetes and underlying respiratory disease. We searched electronic databases for clinical trials of adults randomized to either glucocorticoid treatment or placebo. Eight articles comprising 2121 participants were identified. We performed a random effects meta-analysis to determine relative risks for the associations between glucocorticoid use and both hyperglycaemia and starting hypoglycaemic therapy. In all individuals, the relative risk of hyperglycaemia comparing glucocorticoid treatment with placebo was 1.72 [95% confidence interval (CI) 1.50-2.04; p < .001]. The relative risks in individuals with and those without diabetes were 2.10 (95% CI 0.92-5.02; p = .079) and 1.50 (95% CI 0.79-2.86; p = .22), respectively. In all individuals, the relative risk of hyperglycaemia requiring initiation of hypoglycaemic therapy, comparing glucocorticoid treatment with placebo, was 1.73 (95% CI 1.40-2.14; p < .001). In conclusion, glucocorticoid therapy increases the risk of hyperglycaemia in all individuals with underlying respiratory disease but not when diabetic status is analysed separately.Medical Research Council (Grant ID: MC_UU_12015/1)This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1111/dom.1273

Experimental study of turbulent-jet wave packets and their acoustic efficiency

This paper details the statistical and time-resolved analysis of the relationship between the near-field pressure fluctuations of unforced, subsonic free jets (0.4 ≤ M ≤ 0.6) and their far-field sound emissions. Near-field and far-field microphone measurements were taken on a conical array close to the jets and an azimuthal ring at 20∘ to the jet axis, respectively. Recent velocity and pressure measurements indicate the presence of linear wave packets in the near field by closely matching predictions from the linear homogenous parabolized stability equations, but the agreement breaks down both beyond the end of the potential core and when considering higher order statistical moments, such as the two-point coherence. Proper orthogonal decomposition (POD), interpreted in terms of inhomogeneous linear models using the resolvent framework allows us to understand these discrepancies. A new technique is developed for projecting time-domain pressure measurements onto a statistically obtained POD basis, yielding the time-resolved activity of each POD mode and its correlation with the far field. A single POD mode, interpreted as an optimal high-gain structure that arises due to turbulent forcing, captures the salient near-field–far-field correlation signature; further, the signatures of the next two modes, understood as suboptimally forced structures, suggest that these POD modes represent higher order, acoustically important near-field behavior. An existing Green's-function-based technique is used to make far-field predictions, and results are interpreted in terms of POD/resolvent modes, indicating the acoustic importance of this higher order behavior. The technique is extended to provide time-domain far-field predictions

Craniofacial Syndrome Identification Using Convolutional Mesh Autoencoders

Background: Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. While these are powerful tools for photographic analysis, they are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities, and present an alternative to image-based analysis. // Methods: We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). // Findings: Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation makes it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting. // Interpretation: Our study demonstrates the use of 3D convolutional mesh autoencoders for the diagnosis of syndromic craniosynostosis. The topological nature of the tool presents opportunities for this method to be applied as a diagnostic tool across a number of 3D imaging modalities

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis

Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. They are powerful tools for photographic analysis but are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities and present an alternative to image-based analysis. We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation make it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting

Correlation between head shape and volumetric changes following spring-assisted posterior vault expansion

OBJECTIVE: To investigate whether different head shapes show different volumetric changes following spring-assisted posterior vault expansion (SA-PVE) and to investigate the influence of surgical and morphological parameters on SA-PVE. MATERIALS AND METHODS: Preoperative three-dimensional skull models from patients who underwent SA-PVE were extracted from computed tomography scans. Patient head shape was described using statistical shape modelling (SSM) and principal component analysis (PCA). Preoperative and postoperative intracranial volume (ICV) and cranial index (CI) were calculated. Surgical and morphological parameters included skull bone thickness, number of springs, duration of spring insertion and type of osteotomy. RESULTS: In the analysis, 31 patients were included. SA-PVE resulted in a significant ICV increase (284.1 ± 171.6 cm3, p<0.001) and a significant CI decrease (−2.9 ± 4.3%, p<0.001). The first principal component was significantly correlated with change in ICV (Spearman ρ = 0.68, p<0.001). Change in ICV was significantly correlated with skull bone thickness (ρ = −0.60, p<0.001) and age at time of surgery (ρ = −0.60, p<0.001). No correlations were found between the change in ICV and number of springs, duration of spring insertion and type of osteotomy. CONCLUSION: SA-PVE is effective for increasing the ICV and resolving raised intracranial pressure. Younger, brachycephalic patients benefit more from surgery in terms of ICV increase. Skull bone thickness seems to be a crucial factor and should be assessed to achieve optimal ICV increase. In contrast, insertion of more than two springs, duration of spring insertion or performing a fully cut through osteotomy do not seem to impact the ICV increase. When interpreting ICV increases, normal calvarial growth should be taken into account

Correlation of Intracranial Volume With Head Surface Volume in Patients With Multisutural Craniosynostosis

Intracranial volume (ICV) is an important parameter for monitoring patients with multisutural craniosynostosis. Intracranial volume measurements are routinely derived from computed tomography (CT) head scans, which involves ionizing radiation. Estimation of ICV from head surface volumes could prove useful as 3D surface scanners could be used to indirectly acquire ICV information, using a non-invasive, non-ionizing method.Pre- and postoperative 3D CT scans from spring-assisted posterior vault expansion (sPVE) patients operated between 2008 and 2018 in a single center were collected. Patients were treated for multisutural craniosynostosis, both syndromic and non-syndromic. For each patient, ICV was calculated from the CT scans as carried out in clinical practice. Additionally, the 3D soft tissue surface volume (STV) was extracted by 3D reconstruction of the CT image soft tissue of each case, further elaborated by computer-aided design (CAD) software. Correlations were analyzed before surgery, after surgery, combined for all patients and in syndrome subgroups.Soft tissue surface volume was highly correlated to ICV for all analyses: r = 0.946 preoperatively, r = 0.959 postoperatively, and r = 0.960 all cases combined. Subgroup analyses for Apert, Crouzon-Pfeiffer and complex craniosynostosis were highly significant as well (P < 0.001).In conclusion, 3D surface model volumes correlated strongly to ICV, measured from the same scan, and linear equations for this correlation are provided. Estimation of ICV with just a 3D surface model could thus be realized using a simple method, which does not require radiations and therefore would allow closer monitoring in patients through multiple acquisitions over time

Near-field wavepackets and the far-field sound of a subsonic jet

This paper details the analysis of the relationship between the near-field pressure fluctuations of an unforced, subsonic free jet (0.4 ≤ M ≤ 0.6) and its low-angle, far-field sound emissions. Azimuthal rings of six microphones recorded pressure fluctuations on a conical surface in the jet near field while an azimuthal ring of three microphones recorded fluctuations in the far field at θ = 20° and R/D = 47.1. Recent measurements have shown close agreement between the velocity fluctuations up to the end of the potential core of the currently studied jet and predictions from the linear Parabolised Stability Equations (PSE), indicating the presence of linear wavepackets in the jet velocity field. Solutions of the Linearised Euler Equations (LEE) reported in the present paper also show good agreement with measurements, and provide a first step toward a time-domain description of the said wavepackets. Though the agreement for PSE in the velocity field breaks down downstream of the potential core, Proper Orthogonal Decomposition (POD) of the current results shows that the wavepackets do persist in this region and are clearly apparent in the near pressure field. Attention is then turned to establishing a relationship between these wavepackets and the radiated sound by comparing simultaneously-obtained measurements of the far-field pressure both directly to the near-field signature as well as to numerical predictions of the far-field emissions available from a recent technique using a tailored Green’s function. The direct comparisons are made by correlations between the POD modes and the far-field sound. The first POD mode captures most of the flow energy for the frequency range studied, and the correlation between this mode and the far field is nearly identical to the correlation using the full near-field signal. Higher POD modes also show significant correlation to the far field with a different space–time structure than the first mode. The Green’s function predictions are performed both statistically and in the time domain, and though they are shown to be valid for a near-field array with a long axial extent, the experimental limitation of a shorter array (0.5 ≤ x/D ≤ 8.9), which truncates the wavepacket source in the calculations, causes inaccurate predictions for the experimental data. This error is thought to be the result of a spurious source introduced by the truncation that interferes both constructively and destructively with the wavepacket source. A validation problem shows that this error would be smaller for a higher-M jet

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face

Comparison of 3D Scanner Systems for Craniomaxillofacial Imaging

Two-dimensional photographs are the standard for assessing craniofacial surgery clinical outcomes despite lacking three-dimensional (3D) depth and shape. Therefore, 3D-scanners have been gaining popularity in various fields of plastic and reconstructive surgery, including craniomaxillofacial surgery. Head shapes of eight adult volunteers were acquired with four 3D scanners: 1.5T Avanto MRI, Siemens; 3dMDface System, 3dMD Inc.; M4D Scan, Rodin4D; and Structure Sensor, Occipital Inc. Accuracy was evaluated as percentage of data within a range of 2 mm from the 3DMDface System reconstruction, by surface-to-surface root mean square distances (RMS), and with facial distance maps. Precision was determined with RMS. Relative to the 3dMDface System, accuracy was highest for M4D Scan (90% within 2 mm; RMS of 0.71 mm ± 0.28 mm), then Avanto MRI (86%; 1.11 mm ± 0.33 mm), and Structure Sensor (80%; 1.33 mm ± 0.46). M4D Scan and Structure Sensor precision were 0.50 mm ± 0.04 mm and 0.51 mm ± 0.03 mm. Clinical and technical requirements govern scanner choice, however, 3dMDface System and M4D Scan provide high-quality results. It is foreseeable that compact, hand-held systems become more popular in the near future

Dying on the Streets: Homeless Persons’ Concerns and Desires about End of Life Care

BACKGROUND: There is little understanding about the experiences and preferences at the end of life (EOL) for people from unique cultural and socioeconomic backgrounds. Homeless individuals are extreme examples of these overlooked populations; they have the greatest risk of death, encounter barriers to health care, and lack the resources and relationships assumed necessary for appropriate EOL care. Exploring their desires and concerns will provide insight for the care of this vulnerable and disenfranchised population, as well as others who are underserved. OBJECTIVE: Explore the concerns and desires for EOL care among homeless persons. DESIGN: Qualitative study utilizing focus groups. PARTICIPANTS: Fifty-three homeless persons recruited from agencies providing homeless services. MEASUREMENTS: In-depth interviews, which were audiotaped and transcribed. RESULTS: We present 3 domains encompassing 11 themes arising from our investigation, some of which are previously unreported. Homeless persons worried about dying and EOL care; had frequent encounters with death; voiced many unique fears, such as dying anonymously and undiscovered; favored EOL documentation, such as advance directives; and demonstrated ambivalence towards contacting family. They also spoke of barriers to EOL care and shared interventions to improve dying among the very poor and estranged. CONCLUSIONS: Homeless persons have significant personal experience and feelings about death, dying, and EOL care, much of which is different from those previously described in the EOL literature about other populations. These findings have implications not only for homeless persons, but for others who are poor and disenfranchised