Early intubation versus late intubation for COVID-19 patients: An in situ simulation identifying factors affecting performance and infection control in airway management.

COVID-19 poses an infectious risk to healthcare workers especially during airway management. We compared the impact of early versus late intubation on infection control and performance in a randomised in situ simulation, using fluorescent powder as a surrogate for contamination. Twenty anaesthetists and intensivists intubated a simulated patient with COVID-19. The primary outcome was the degree of contamination. The secondary outcomes included the use of bag-valve-mask ventilation, the incidence of manikin cough, intubation time, first attempt success and heart rate variability as a measure of stress. The contamination score was significantly increased in the late intubation group, mean (standard deviation, SD) 17.20 (6.17), 95% confidence intervals (CI) 12.80 to 21.62 versus the early intubation group, mean (SD) 9.90 (5.13), 95% CI 6.23 to 13.57, P = 0.005. The contamination score was increased after simulated cough occurrence (mean (SD) 18.0 (5.09) versus 5.50 (2.10) in those without cough; P<0.001), and when first attempt laryngoscopy failed (mean (SD) of 17.1 (6.41) versus 11.6 (6.20) P = 0.038). The incidence of bag-mask ventilation was higher in the late intubation group (80% versus 30%; P=0.035). There was no significant difference in intubation time, incidence of failed first attempt laryngoscopy or heart rate variability between the two groups. Late intubation in patients with COVID-19 may be associated with greater laryngoscopist contamination and potential aerosol-generating events compared with early intubation. There was no difference in performance measured by intubation time and incidence of first attempt success. Late intubation, especially when resources are limited, may be a valid approach. However, strict infection control and appropriate personal protective equipment usage is recommended in such cases.

Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT.

OBJECTIVE: This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans. METHODS: Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images (i.e., atlases) were pairwise registered based on intensity information with six registration tools (FSL, ANTS-CC, ANTS-QUICK-MI, IRTK, NIFTYREG, and DEEDS). The Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. Permutation tests and indifference-zone ranking were performed to examine the statistical and practical significance, respectively. RESULTS: The results suggest that DEEDS yielded the best registration performance. However, due to the overall low DSC values, and substantial portion of low-performing outliers, great care must be taken when image registration is used for local interpretation of abdominal CT. CONCLUSION: There is substantial room for improvement in image registration for abdominal CT. SIGNIFICANCE: All data and source code are available so that innovations in registration can be directly compared with the current generation of tools without excessive duplication of effort.

Evaluation of Body-Wise and Organ-Wise Registrations For Abdominal Organs.

Identifying cross-sectional and longitudinal correspondence in the abdomen on computed tomography (CT) scans is necessary for quantitatively tracking change and understanding population characteristics, yet abdominal image registration is a challenging problem. The key difficulty in solving this problem is huge variations in organ dimensions and shapes across subjects. The current standard registration method uses the global or body-wise registration technique, which is based on the global topology for alignment. This method (although producing decent results) has substantial influence of outliers, thus leaving room for significant improvement. Here, we study a new image registration approach using local (organ-wise registration) by first creating organ-specific bounding boxes and then using these regions of interest (ROIs) for aligning references to target. Based on Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD) and Hausdorff Distance (HD), the organ-wise approach is demonstrated to have significantly better results by minimizing the distorting effects of organ variations. This paper compares exclusively the two registration methods by providing novel quantitative and qualitative comparison data and is a subset of the more comprehensive problem of improving the multi-atlas segmentation by using organ normalization.

Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.

Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.

Image registration has become an essential image processing technique to compare data across time and individuals. With the successes in volumetric brain registration, general-purpose software tools are beginning to be applied to abdominal computed tomography (CT) scans. Herein, we evaluate five current tools for registering clinically acquired abdominal CT scans. Twelve abdominal organs were labeled on a set of 20 atlases to enable assessment of correspondence. The 20 atlases were pairwise registered based on only intensity information with five registration tools (affine IRTK, FNIRT, Non-Rigid IRTK, NiftyReg, and ANTs). Following the brain literature, the Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. However, interpretation was confounded due to a significant proportion of outliers. Examining the retrospectively selected top 1 and 5 atlases for each target revealed that there was a substantive performance difference between methods. To further our understanding, we constructed majority vote segmentation with the top 5 DSC values for each organ and target. The results illustrated a median improvement of 85% in DSC between the raw results and majority vote. These experiments show that some images may be well registered to some targets using the available software tools, but there is significant room for improvement and reveals the need for innovation and research in the field of registration in abdominal CTs. If image registration is to be used for local interpretation of abdominal CT, great care must be taken to account for outliers (e.g., atlas selection in statistical fusion).

Efficient Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.

Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models.

Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid/gray matter/white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms. In the context of variable abdominal anatomy and rich algorithms, it is difficult to assess the marginal contribution of GMM. Herein, we characterize the efficacy of an a posteriori framework that integrates GMM of organ-wise intensity likelihood with spatial priors from multiple target-specific registered labels. In our study, we first manually labeled 100 CT images. Then, we assigned 40 images to use as training data for constructing target-specific spatial priors and intensity likelihoods. The remaining 60 images were evaluated as test targets for segmenting 12 abdominal organs. The overlap between the true and the automatic segmentations was measured by Dice similarity coefficient (DSC). A median improvement of 145% was achieved by integrating the GMM intensity likelihood against the specific spatial prior. The proposed framework opens the opportunities for abdominal organ segmentation by efficiently using both the spatial and appearance information from the atlases, and creates a benchmark for large-scale automatic abdominal segmentation.

Total synthesis of the potent microtubule-stabilizing agent (+)-discodermolide.

The total synthesis of the potent microtubule-stabilizing, antimitotic agent (+)-discodermolide is described. The convergent synthetic strategy takes advantage of the diastereoselective alkylation of a ketone enolate to establish the key C15-C16 bond. The synthesis is amenable to preparation of gram-scale quantities of (+)-discodermolide and analogues.

The incidence of cutaneous larva migrans in Montserrat, Leeward Islands, West Indies

This report describes a retrospective analysis of case records of cutaneous larva migrans in Montserrat. An estimated incidence of 0.064 percent was found between mid-1977 and mid-1978 (AU)

The presence of Schistosoma mansoni in Montserrat Leeward Islands

Following the discovery in two villages in Montserrat of five individuals who were passing Schistosoma mansoni ova in their stools, a house to house survey was undertaken in November 1978 to determine the prevalence of the infection. From 158 persons residing in the two villages 137 faecal samples were received. Of these 14 percent were positive for S. mansoni ova. The enzyme linked immunosorbent assay (ELISA) was performed on finger-prick blood samples collected on chromatography paper. In the ELISA 16 percent of the 132 persons tested were positive. In comparison with the stool examination, the ELISA showed a 53 percent sensitivity and a 92 percent specificity. It was recommended that the individuals passing schistosome eggs should be treated.(AU)