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

Two-Center Review of Posterior Vault Expansion following a Staged or Expectant Treatment of Crouzon and Apert Craniosynostosis

Background: The timing of posterior cranial expansion for the management of intracranial pressure can be "staged" by age and dysmorphology or "expectant" by pressure monitoring. The authors report shared outcome measures from one center performing posterior vault remodeling (PCVR) or distraction (PVDO) following a staged approach and another performing spring-assisted expansion (SAPVE) following an expectant protocol. Methods: Apert or Crouzon syndrome patients who underwent posterior expansion younger than 2 years were included. Perioperative outcomes and subsequent cranial operations were recorded up to last follow-up and intracranial volume changes measured and adjusted using growth curves. Results: Thirty-eight patients were included. Following the expectant protocol, Apert patients underwent SAPVE at a younger age (8 months) than Crouzon patients (16 months). The initial surgery time was shorter but total operative time, including device removal, was longer for PVDO (3 hours 52 minutes) and SAPVE (4 hours 34 minutes) than for PCVR (3 hours 24 minutes). Growth-adjusted volume increase was significant and comparable. Fourteen percent of PCVR, 33% of PVDO, and 11% of SAPVE cases had complications, but without long-term deficits. Following the staged approach, 5% underwent only PVDO, 85% had a staged posterior followed by anterior surgery, and 10% required a third expansion. Following the expectant approach, 42% of patients had only posterior expansion at last follow-up, 32% had a secondary cranial surgery, and 26% had a third cranial expansion. Conclusion: Two approaches involving posterior vault expansion in young syndromic patients using three techniques resulted in comparable early volume expansion and complication profiles. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.</p

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

Does the Mutation Type Affect the Response to Cranial Vault Expansion in Children With Apert Syndrome?

Most cases of Apert syndrome are caused by mutations in the FGFR2 gene, either Ser252Trp or Pro253Arg. In these patients, over the last decades, spring-assisted posterior vault expansion (SA-PVE) has been the technique of choice for cranial vault expansion in the Craniofacial Unit of Great Ormond Street Hospital for Children (GOSH), London. The aim of this study was to investigate if there is a difference in preoperative intracranial volume (ICV) in patients with Apert syndrome with Ser252Trp or Pro253Arg mutation and whether these mutations affect the change in ICV achieved by SA-PVE. The GOSH craniofacial SA-PVE database was used to select patients with complete genetic testing and preoperative and postoperative computed tomography scans. ICV was calculated using FSL (FMRIB Analysis Group, Oxford) and adjusted based on Apert-specific growth curves. Sixteen patients were included with 8 having Ser252Trp mutation and 8 having Pro253Arg mutation. The mean preoperative adjusted computed tomography volume for patients in the Ser252Trp group was 1137.7 cm3 and in the Pro253Arg group was 1115.8 cm3 (P=1.00). There was a significant increase in ICV following SA-PVE in all patients (P<0.001) with no difference in mean change in ICV between the groups (P=0.51). Four (50%) patients with Ser252Trp mutation and 3 (37.5%) with Pro253Arg mutations required a second operation after primary SA-PVE. The results demonstrate that regardless of the mutation present, SA-PVE was successful in increasing ICV in patients with Apert syndrome and that a repeat volume expanding procedure was required by a similar number of patients in the 2 groups

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

Electrophysiological and fundoscopic detection of intracranial hypertension in craniosynostosis

Aims: To assess the diagnostic accuracy of fundoscopy and visual evoked potentials (VEPs) in detecting intracranial hypertension (IH) in patients with craniosynostosis undergoing spring-assisted posterior vault expansion (sPVE). Methods: Children with craniosynostosis undergoing sPVE and 48-hour intracranial pressure (ICP) monitoring were included in this single-centre, retrospective, diagnostic accuracy study. Data for ICP, fundoscopy and VEPs were analysed. Primary outcome measures were papilloedema on fundoscopy, VEP assessments and IH, defined as mean ICP &gt; 20 mmHg. Diagnostic indices were calculated for fundoscopy and VEPs against IH. Secondary outcome measures included final visual outcomes. Results: Fundoscopic examinations were available for 35 children and isolated VEPs for 30 children, 22 of whom had at least three serial VEPs. Sensitivity was 32.1% for fundoscopy (95% confidence intervals [CI]: 15.9–52.4) and 58.3% for isolated VEPs (95% CI 36.6–77.9). Specificity for IH was 100% for fundoscopy (95% CI: 59.0–100) and 83.3% for isolated VEPs (95% CI: 35.9–99.6). Where longitudinal deterioration was suspected from some prVEPs but not corroborated by all, sensitivity increased to 70.6% (95% CI: 44.0–89.7), while specificity decreased to 60% (95% CI: 14.7–94.7). Where longitudinal deterioration was clinically significant, sensitivity decreased to 47.1% (23.0–72.2) and specificity increased to 100% (47.8–100). Median final BCVA was 0.24 logMAR (n = 36). UK driving standard BCVA was achieved by 26 patients (72.2%), defined as ≥0.30 logMAR in the better eye. Conclusion: Papilloedema present on fundoscopy reliably indicated IH, but its absence did not exclude IH. VEP testing boosted sensitivity at the expense of specificity, depending on method of analysis.</p

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