Aortitis: recent advances, current concepts and future possibilities

Broadly defined, aortitis refers to inflammation of the aorta and incorporates both infectious and non-infectious aetiologies. As advanced imaging modalities are increasingly incorporated into clinical practice, the phenotypic spectrum associated with aortitis has widened. The primary large vessel vasculitides, giant cell arteritis and Takayasu arteritis, are the most common causes of non-infectious aortitis. Aortitis without systemic disease or involvement of other vascular territories is classified as clinically isolated aortitis. Periaortitis, where inflammation spreads beyond the aortic wall, is an important disease subset with a distinct group of aetiologies. Infectious aortitis can involve bacterial, viral or fungal pathogens and, while uncommon, can be devastating. Importantly, optimal management strategies and patient outcomes differ between aortitis subgroups highlighting the need for a thorough diagnostic workup. Monitoring disease activity over time is also challenging as normal inflammatory markers do not exclude significant vascular inflammation, particularly after starting treatment. Additional areas of unmet clinical need include clear disease classifications and improved short-term and long-term management strategies. Some of these calls are now being answered, particularly with regard to large vessel vasculitis where our understanding has advanced significantly in recent years. Work extrapolated from temporal artery histology has paved the way for targeted biological agents and, although glucocorticoids remain central to the management of non-infectious aortitis, these may allow reduced glucocorticoid reliance. Future work should seek to clarify disease definitions, improve diagnostic pathways and ultimately allow a more stratified approach to patient management

Cardiovascular outcomes in patients with chronic kidney disease and COVID-19:a multi-regional data-linkage study

BACKGROUND: Data describing cardiovascular outcomes in patients with COVID-19 and chronic kidney disease (CKD) are lacking. We compared cardiovascular outcomes of patients with and without COVID-19, stratified by CKD status. METHODS: This retrospective, multi-regional data-linkage study utilised individual patient-level data from two Scottish cohorts. All patients tested for SARS-CoV-2 in Cohort 1 between 01/02/2020 and 31/03/2021, and in Cohort 2 between 28/02/2020 and 08/02/2021, were included. RESULTS: Overall, 86 964 patients were tested for SARS-CoV-2. There were 36 904 patients (61±21 years, 58.1% women, 15.9% CKD, 10.1% COVID-19 positive) in Cohort 1 and 50 060 patients (63±20 years, 62.0% women, 16.4% CKD, 9.1% COVID-19 positive) in Cohort 2. In CKD patients, COVID-19 increased the risk of cardiovascular death by more than two-fold within 30 days (cause-specific hazard ratio [csHR] meta-estimate 2.34, 95% confidence interval [CI] 1.83–2.99), and by 57% at the end of follow-up (csHR meta-estimate 1.57, 95% CI 1.31–1.89). Similarly, the risk of all-cause death in COVID-19 positive versus negative CKD patients was greatest within 30 days (HR 4.53, 95% CI 3.97–5.16). Compared to patients without CKD, those with CKD had a higher risk of testing positive (11.5% versus 9.3%). Following a positive test, CKD patients had higher rates of cardiovascular death (11.1% versus 2.7%), cardiovascular complications, and cardiovascular hospitalisations (7.1% versus 3.3%) than those without CKD. CONCLUSIONS: COVID-19 increases the risk of cardiovascular and all-cause death in CKD patients, especially in the short-term. CKD patients with COVID-19 are also at a disproportionate risk of cardiovascular complications than those without CKD

Utility of interval kidney biopsy in ANCA-associated vasculitis

OBJECTIVES: ANCA-associated vasculitis (AAV) is a rare autoimmune disorder that commonly involves the kidney. Early identification of kidney involvement, assessing treatment-response and predicting outcome are important clinical challenges. Here, we assessed the potential utility of interval kidney biopsy in AAV. METHODS: In a tertiary referral centre with a dedicated vasculitis service, we identified patients with AAV who had undergone interval kidney biopsy, defined as a repeat kidney biopsy (following an initial biopsy showing active AAV) undertaken to determine the histological response in the kidney following induction immunosuppression. We analysed biochemical, histological and outcome data, including times to kidney failure and death for all patients. RESULTS: We identified 57 patients with AAV who underwent at least one interval kidney biopsy (59 interval biopsies in total; median time to interval biopsy ∼130 days). Of the 59 interval biopsies performed, 24 (41%) patients had clinically suspected active disease at time of biopsy which was confirmed histologically in only 42% of cases; 35 (59%) patients were in clinical disease-remission, and this was correct in 97% of cases. The clinician’s impression was incorrect in one in four patients. Hematuria at interval biopsy did not correlate with histological activity. Interval biopsy showed fewer acute lesions and more chronic damage compared with initial biopsy and led to immunosuppressive treatment-change in 75% (44/59) of patients. Clinical risk prediction tools tended to operate better using interval biopsy data. CONCLUSION: Interval kidney biopsy is useful for determining treatment-response and subsequent disease management in AAV. It may provide better prognostic information than initial kidney biopsy and should be considered for inclusion into future clinical trials and treatment protocols for patients with AAV

An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography

Purpose: To develop an open-source, fully-automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from 3 clinical studies related to systemic disease. Ground truth segmentations were generated using a clinically validated, semi-automatic choroid segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a UNet with MobileNetV3 backbone pre-trained on ImageNet. Standard segmentation agreement metrics, as well as derived measures of choroidal thickness and area, were used to evaluate DeepGPET, alongside qualitative evaluation from a clinical ophthalmologist. Results: DeepGPET achieves excellent agreement with GPET on data from 3 clinical studies (AUC=0.9994, Dice=0.9664; Pearson correlation of 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34.49s ($\pm$15.09) using GPET to 1.25s ($\pm$0.10) using DeepGPET. Both methods performed similarly according to a clinical ophthalmologist, who qualitatively judged a subset of segmentations by GPET and DeepGPET, based on smoothness and accuracy of segmentations. Conclusions :DeepGPET, a fully-automatic, open-source algorithm for choroidal segmentation, will enable researchers to efficiently extract choroidal measurements, even for large datasets. As no manual interventions are required, DeepGPET is less subjective than semi-automatic methods and could be deployed in clinical practice without necessitating a trained operator. DeepGPET addresses the lack of open-source, fully-automatic and clinically relevant choroid segmentation algorithms, and its subsequent public release will facilitate future choroidal research both in ophthalmology and wider systemic health.Comment: 8 pages, 2 figures, 3 tables. Currently in submission to ARVO TVST (Association for Research in Vision and Ophthalmology, Translational Vision Science & Technology). GitHub link to codebase provided upon publicatio

Choroidal and retinal thinning in chronic kidney disease independently associate with eGFR decline and are modifiable with treatment

In patients with chronic kidney disease (CKD), there is an unmet need for novel biomarkers that reliably track kidney injury, demonstrate treatment-response, and predict outcomes. Here, we investigated the potential of retinal optical coherence tomography (OCT) to achieve these ends in a series of prospective studies of patients with pre-dialysis CKD (including those with a kidney transplant), patients with kidney failure undergoing kidney transplantation, living kidney donors, and healthy volunteers. Compared to health, we observed similar retinal thinning and reduced macular volume in patients with CKD and a kidney transplant. However, choroidal thinning in CKD was not seen in patients with a kidney transplant whose choroids resembled those of healthy volunteers. In CKD, the degree of choroidal thinning related to falling eGFR and extent of kidney scarring. Following kidney transplantation, choroidal thickness increased rapidly (~10%) and was maintained over 1-year, whereas gradual choroidal thinning was observed during the 12 months following kidney donation. In patients with CKD, retinal and choroidal thickness independently associated with eGFR decline over 2 years. These observations highlight the potential for retinal OCT to act as a non-invasive monitoring and prognostic biomarker of kidney injury

Can a “state of the art” chemistry transport model simulate Amazonian tropospheric chemistry?

We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr^(−1) exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30–85°W, 14°N–25°S) contributes about 15–35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NO_x emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10–100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns