Safety of maintaining elective and emergency surgery during the COVID-19 pandemic with the introduction of a Protected Elective Surgical Unit (PESU): A cross-specialty evaluation of 30-day outcomes in 9,925 patients undergoing surgery in a University Health Board

Background

The COVID-19 pandemic has caused unprecedented health care challenges mandating surgical service reconfiguration. Within our hospital, emergency and elective streams were separated and self-contained Protected Elective Surgical Units were developed to mitigate against infection-related morbidity. Aims of this study were to determine the risk of COVID-19 transmission and mortality and whether the development of Protected Elective Surgical Units can result in significant reduction in risk.

Methods

A retrospective observational study of consecutive patients from 18 specialties undergoing elective or emergency surgery under general, spinal, or epidural anaesthetic over a 12-month study period was undertaken. Primary outcome measures were 30-day postoperative COVID-19 transmission rate and mortality. Secondary adjusted analyses were performed to ascertain hospital and Protected Elective Surgical Unit transmission rates.

Results

Between 15 March 2020 and 14 March 2021, 9,925 patients underwent surgery: 6,464 (65.1%) elective, 5,116 (51.5%) female, and median age 57 (39–70). A total of 69.5% of all procedures were performed in Protected Elective Surgical Units. Overall, 30-day postoperative COVID-19 transmission was 2.8% (3.4% emergency vs 1.2% elective P < .001). Protected Elective Surgical Unit postoperative transmission was significantly lower than non–Protected Elective Surgical Unit (0.42% vs 3.2% P < .001), with an adjusted likely in-hospital Protected Elective Surgical Unit transmission of 0.04%. The 30-day all-cause mortality was 1.7% and was 14.6% in COVID-19–positive patients. COVID-19 infection, age > 70, male sex, American Society of Anesthesiologists grade > 2, and emergency surgery were all independently associated with mortality.

Conclusion

This study has demonstrated that Protected Elective Surgical Units can facilitate high-volume elective surgical services throughout peaks of the COVID-19 pandemic while minimising viral transmission and mortality. However, mortality risk associated with perioperative COVID-19 infection remains high.

276 Safety of Maintaining Elective and Emergency Surgery During the COVID-19 Pandemic with the Introduction of an Innovative Protected Elective Surgical Unit (PESU): A Cross-Specialty Evaluation of 30-Day Outcomes in 9925 Patients Undergoing Surgery in a University Health Board

Aim

High quality mobile health applications (mhealth apps) have the potential to enhance the prevention, diagnosis, and treatment of burns. The primary aim of this study was to evaluate whether the quality of mhealth apps for burns care is being adequately assessed. The secondary aim was to determine whether these apps meet UK regulatory standards.

Method

We searched AMED, BNI, CINAHL, Cochrane library, Embase, Emcare, Medline and PsychInfo to identify studies assessing mhealth app quality for burns. The PRISMA reporting guideline was adhered to. Two independent reviewers screened s to identify relevant studies. We analysed whether seven established domains of mhealth app quality were assessed: design, information/content, usability, functionality, ethical issues, security/privacy, and user-perceived value.

Results

Of the 28 included studies, none assessed all seven domains of quality. Design was assessed in 4/28 studies; information/content in 26/28 studies; usability in 12/28 studies; functionality in 10/28 studies; ethical issues were never assessed in any studies; security/privacy was not assessed; subjective assessment was made in 9/28 studies. 17/28 studies included apps that met the definition of ‘medical device’ according to MHRA guidance, yet only one app was appropriately certified with the UK Conformity Assessed (UKCA) mark.

Conclusions

The quality of mHealth apps for burns are not being adequately assessed. The majority of apps should be considered medical devices according to UK standards, but only one was appropriately certified. Regulatory bodies should support mhealth app developers, so as to improve quality control whilst simultaneously fostering innovation.

A deep learning approach to generate contrast-enhanced computerised tomography angiograms without the use of intravenous contrast agents

Introduction

Contrast-enhanced computerised tomographic (CT) angiograms are widely used in cardiovascular imaging to obtain a non-invasive view of arterial structures. In aortic aneurysmal disease (AAA), CT angiograms are required prior to surgical intervention to differentiate between blood and the intra-luminal thrombus, which is present in 95% of cases. However, contrast agents are associated with complications at the injection site as well as renal toxicity leading to contrast-induced nephropathy (CIN) and renal failure.

Purpose

We hypothesised that the raw data acquired from a non-contrast CT contains sufficient information to differentiate blood and other soft tissue components. Therefore, we utilised deep learning methods to define the subtleties between the various components of soft tissue in order to simulate contrast enhanced CT images without the need of contrast agents.

Methods

Twenty-six AAA patients with paired non-contrast and contrast-enhanced CT images were randomly selected from an ethically approved ongoing study (Ethics Ref 13/SC/0250) and used for model training and evaluation (13/13). Non-contrast axial slices within the aneurysmal region from 10 patients (n=100) were sampled for the underlying Hounsfield unit (HU) distribution at the lumen, intra-luminal thrombus and interface locations, identified from their paired contrast axial slices. Subsequently, paired axial slices within the training cohort were augmented in a ratio of 10:1 to produce a total of 23,551 2-D images. We trained a 2-D Cycle Generative Adversarial Network (cycleGAN) for this non-contrast to contrast transformation task. Model output was assessed by comparison to the contrast image, which serves as a gold standard, using image similarity metrics (ex. SSIM Index).

Results

Sampling HUs within the non-contrast CT scan across multiple axial slices (Figure 1A) revealed significant differences between the blood flow lumen (yellow), blood/thrombus interface (red), and thrombus (blue) regions (p&lt;0.001 for all comparisons). This highlighted the intrinsic differences between the regions and established the foundation for subsequent deep learning methods. The Non-Contrast-to-Contrast (NC2C)-cycleGAN was trained with a learning rate of 0.0002 for 200 epochs on 256 x 256 images centred around the aorta. Figure 1B depicts “contrast-enhanced” images generated from non-contrast CT images across the aortic length from the testing cohort. This preliminary model is able to differentiate between the lumen and intra-luminal thrombus of aneurysmal sections with reasonable resemblance to the ground truth.

Conclusion

This study describes, for the first time, the ability to differentiate between visually incoherent soft tissue regions in non-contrast CT images using deep learning methods. Ultimately, refinement of this methodology may negate the use of intravenous contrast and prevent related complications.

CTA Generation from Non-Contrast CTs

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): Clarendon

Identification and fine mapping of a new gene, BPH31 conferring resistance to brown planthopper biotype 4 of India to improve rice, Oryza sativa L

BACKGROUND

Rice (Oryza sativa L.) is the staple food for more than 3.5 billion people, mainly in Asia. Brown planthopper (BPH) is one of the most destructive insect pests of rice that limits rice production. Host-plant resistance is one of the most efficient ways to overcome BPH damage to the rice crop.

RESULTS

BPH bioassay studies from 2009 to 2015 conducted in India and at the International Rice Research Institute (IRRI), Philippines, revealed that the cultivar CR2711-76 developed at the National Rice Research Institute (NRRI), Cuttack, India, showed stable and broad-spectrum resistance to several BPH populations of the Philippines and BPH biotype 4 of India. Genetic analysis and fine mapping confirmed the presence of a single dominant gene, BPH31, in CR2711-76 conferring BPH resistance. The BPH31 gene was located on the long arm of chromosome 3 within an interval of 475 kb between the markers PA26 and RM2334. Bioassay analysis of the BPH31 gene in CR2711-76 was carried out against BPH populations of the Philippines. The results from bioassay revealed that CR2711-76 possesses three different mechanisms of resistance: antibiosis, antixenosis, and tolerance. The effectiveness of flanking markers was tested in a segregating population and the InDel type markers PA26 and RM2334 showed high co-segregation with the resistance phenotype. Foreground and background analysis by tightly linked markers as well as using the Infinium 6 K SNP chip respectively were applied for transferring the BPH31 gene into an indica variety, Jaya. The improved BPH31-derived Jaya lines showed strong resistance to BPH biotypes of India and the Philippines.

CONCLUSION

The new BPH31 gene can be used in BPH resistance breeding programs on the Indian subcontinent. The tightly linked DNA markers identified in the study have proved their effectiveness and can be utilized in BPH resistance breeding in rice.