The prognostic value of metabolic dysfunction-associated steatotic liver disease in acute myocardial infarction: A propensity score-matched analysis.

AIM: Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are at increased risk of incident cardiovascular disease. However, the clinical characteristics and prognostic importance of MASLD in patients presenting with acute myocardial infarction (AMI) have yet to be examined. METHODS: This study compared the characteristics and outcomes of patients with and without MASLD presenting with AMI at a tertiary centre in Singapore. MASLD was defined as hepatic steatosis, with at least one of five metabolic criteria. Hepatic steatosis was determined using the Hepatic Steatosis Index. Propensity score matching was performed to adjust for age and sex. The Kaplan-Meier curve was constructed for long-term all-cause mortality. Cox regression analysis was used to investigate independent predictors of long-term all-cause mortality. RESULTS: In this study of 4446 patients with AMI, 2223 patients with MASLD were matched with patients without MASLD using propensity scores. The mean follow-up duration was 3.4 ± 2.4 years. The MASLD group had higher rates of obesity, diabetes and chronic kidney disease than their counterparts. Patients with MASLD had early excess all-cause mortality (6.8% vs. 3.6%, p < .001) at 30 days, with unfavourable mortality rates sustained in the long-term (18.3% vs. 14.5%, p = .001) compared with those without MASLD. After adjustment, MASLD remained independently associated with higher long-term all-cause mortality (hazard ratio 1.330, 95% confidence interval 1.106-1.598, p = .002). CONCLUSION: MASLD embodies a higher burden of metabolic dysfunction and is an independent predictor of long-term mortality in the AMI population. Its early identification may be beneficial for risk stratification and provide therapeutic targets for secondary preventive strategies in AMI.

Time trends in preemptive kidney transplantation in Europe: an era registry study.

BACKGROUND: Preemptive kidney transplantation has better outcomes when compared to transplantation after dialysis. We aimed to examine trends in preemptive kidney transplantation between 2000 and 2019 in Europe and to provide an overview of associated policies, barriers and initiatives. METHODS: Adult patients from 12 European countries who received a preemptive kidney transplant were included. The representatives of the registries providing these data were questioned on the policies, barriers and initiatives around preemptive kidney transplantation. RESULTS: Between 2000 and 2019, 20 251 adults underwent preemptive kidney transplantation (11 169 from living donors, 8937 from deceased donors). The proportion of first kidney transplantations that were preemptive more than doubled from 7% in 2000 to 18% in 2019, reflecting a similar relative increase for living donor kidney recipients (from 21% to 43%) and deceased donor kidney recipients (from 4% to 11%). Large international differences were found. The increase in preemptive kidney transplantation was observed across all age, sex and primary renal disease groups. Countries had similar criteria for preemptive waitlisting. Barriers mentioned included donor shortage, late referral to the transplant center and long donor or recipient work-up. Suggested initiatives included raising awareness on the possibility of preemptive kidney transplantation, earlier start and shorter work-up time for recipient and living donor. CONCLUSIONS: Over the last two decades the proportion of patients receiving a first kidney transplant preemptively has more than doubled, reflecting a similar relative increase for living and deceased donor kidney recipients.

Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac.

BACKGROUND: Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. PURPOSE: To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. METHODS: Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. RESULTS: The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. CONCLUSIONS: This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking.

CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.

Background: Radiomics analysis could provide complementary tissue characterization in ovarian cancer (OC). However, OC segmentation required in radiomics analysis is time-consuming and labour-intensive. In this study, we aim to evaluate the performance of deep learning-based segmentation of OC on contrast-enhanced CT images and the stability of radiomics features extracted from the automated segmentation. Methods: Staging abdominopelvic CT images of 367 patients with OC were retrospectively recruited. The training and cross-validation sets came from center A (n=283), and testing set (n=84) came from centers B and C. The tumours were manually delineated by a board-certified radiologist. Four model architectures provided by no-new-Net (nnU-Net) method were tested in this task. The segmentation performance evaluated by Dice score, Jaccard score, sensitivity and precision were compared among 4 architectures. The Pearson correlation coefficient (ρ), concordance correlation coefficient (ρc) and Bland-Altman plots were used to evaluate the volumetric assessment of OC between manual and automated segmentations. The stability of extracted radiomics features was evaluated by intraclass correlation coefficient (ICC). Results: The 3D U-Net cascade architecture achieved highest median Dice score, Jaccard score, sensitivity and precision for OC segmentation in the testing set, 0.941, 0.890, 0.973 and 0.925, respectively. Tumour volumes of manual and automated segmentations were highly correlated (ρ=0.944 and ρc =0.933). 85.0% of radiomics features had high correlation with ICC >0.8. Conclusions: The presented deep-learning segmentation could provide highly accurate automated segmentation of OC on CT images with high stability of the extracted radiomics features, showing the potential as a batch-processing segmentation tool.

Seasonal mortality trends for hospitalised patients with acute kidney injury across England.

BACKGROUND: Incidence of acute kidney injury (AKI) is known to peak in winter months. This is likely influenced by seasonality of commonly associated acute illnesses. We set out to assess seasonal mortality trends for patients who develop AKI across the English National Health Service (NHS) and to better understand associations with patient 'case-mix'. METHODS: The study cohort included all hospitalised adult patients in England who triggered a biochemical AKI alert in 2017. We modelled the impact of season on 30-day mortality using multivariable logistic regression; adjusting for age, sex, ethnicity, index of multiple deprivation (IMD), primary diagnosis, comorbidity (RCCI), elective/emergency admission, peak AKI stage and community/hospital acquired AKI. Seasonal odds ratios for AKI mortality were then calculated and compared across individual NHS hospital trusts. RESULTS: The crude 30-day mortality for hospitalised AKI patients was 33% higher in winter compared to summer. Case-mix adjustment for a wide range of clinical and demographic factors did not fully explain excess winter mortality. The adjusted odds ratio of patients dying in winter vs. summer was 1.25 (1.22-1.29), this was higher than for Autumn and Spring vs. Summer, 1.09 (1.06-1.12) and 1.07 (1.04-1.11) respectively and varied across different NHS trusts (9 out of 90 centres outliers). CONCLUSION: We have demonstrated an excess winter mortality risk for hospitalised patients with AKI across the English NHS, which could not be fully explained by seasonal variation in patient case-mix. Whilst the explanation for worse winter outcomes is not clear, unaccounted differences including 'winter-pressures' merit further investigation.

Dependency of NELF-E-SLUG-KAT2B epigenetic axis in breast cancer carcinogenesis.

Cancer cells undergo transcriptional reprogramming to drive tumor progression and metastasis. Using cancer cell lines and patient-derived tumor organoids, we demonstrate that loss of the negative elongation factor (NELF) complex inhibits breast cancer development through downregulating epithelial-mesenchymal transition (EMT) and stemness-associated genes. Quantitative multiplexed Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins (qPLEX-RIME) further reveals a significant rewiring of NELF-E-associated chromatin partners as a function of EMT and a co-option of NELF-E with the key EMT transcription factor SLUG. Accordingly, loss of NELF-E leads to impaired SLUG binding on chromatin. Through integrative transcriptomic and genomic analyses, we identify the histone acetyltransferase, KAT2B, as a key functional target of NELF-E-SLUG. Genetic and pharmacological inactivation of KAT2B ameliorate the expression of EMT markers, phenocopying NELF ablation. Elevated expression of NELF-E and KAT2B is associated with poorer prognosis in breast cancer patients, highlighting the clinical relevance of our findings. Taken together, we uncover a crucial role of the NELF-E-SLUG-KAT2B epigenetic axis in breast cancer carcinogenesis.

Nesprin-1 LINC complexes recruit microtubule cytoskeleton proteins and drive pathology in Lmna-mutant striated muscle.

Mutations in LMNA, the gene encoding A-type lamins, cause laminopathies-diseases of striated muscle and other tissues. The aetiology of laminopathies has been attributed to perturbation of chromatin organization or structural weakening of the nuclear envelope (NE) such that the nucleus becomes more prone to mechanical damage. The latter model requires a conduit for force transmission to the nucleus. NE-associated Linker of Nucleoskeleton and Cytoskeleton (LINC) complexes are one such pathway. Using clustered regularly interspaced short palindromic repeats to disrupt the Nesprin-1 KASH (Klarsicht, ANC-1, Syne Homology) domain, we identified this LINC complex protein as the predominant NE anchor for microtubule cytoskeleton components, including nucleation activities and motor complexes, in mouse cardiomyocytes. Loss of Nesprin-1 LINC complexes resulted in loss of microtubule cytoskeleton proteins at the nucleus and changes in nuclear morphology and positioning in striated muscle cells, but with no overt physiological defects. Disrupting the KASH domain of Nesprin-1 suppresses Lmna-linked cardiac pathology, likely by reducing microtubule cytoskeleton activities at the nucleus. Nesprin-1 LINC complexes thus represent a potential therapeutic target for striated muscle laminopathies.

Targeting the molecular & cellular pillars of human aging with exercise.

Biological aging is the main driver of age-associated chronic diseases. In 2014, the United States National Institute of Aging (NIA) sponsored a meeting between several investigators in the field of aging biology, who identified seven biological pillars of aging and a consensus review, "Geroscience: Linking Aging to Chronic Disease," was published. The pillars of aging demonstrated the conservation of aging pathways in diverse model organisms and thus represent a useful framework with which to study human aging. In this present review, we revisit the seven pillars of aging from the perspective of exercise and discuss how regular physical exercise can modulate these pillars to stave off age-related chronic diseases and maintain functional capacity.

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.

Importance: Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication. Objective: To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets. Design, Setting, and Participants: In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma. Exposures: Contrast-enhanced CT-based radiomics. Main Outcomes and Measures: Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve. Results: In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort. Conclusions and Relevance: In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

A genomic-augmented multivariate prognostic model for the survival of natural-killer/T-cell lymphoma patients from an international cohort.

With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein-Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07-6.73; p < .001) and overall survival (OS, HR: 5.23, 95% CI 2.57-10.65; p = .001) with known risk-features of these indices. When we assign an additional risk-score to samples, which are mutant for the GPM, the Harrell's C-indices of GPM-augmented IPI, PINK, and PINK-E improved significantly (p < .001, χ2 test) for both PFS and OS. Thus, we report on how genomic mutational information could steer toward better prognostication of NKTCL patients.

Autophagy and the hallmarks of aging.

Autophagy, an essential cellular process that mediates degradation of proteins and organelles in lysosomes, has been tightly linked to cellular quality control for its role as part of the proteostasis network. The current interest in identifying the cellular and molecular determinants of aging, has highlighted the important contribution of malfunctioning of autophagy with age to the loss of proteostasis that characterizes all old organisms. However, the diversity of cellular functions of the different types of autophagy and the often reciprocal interactions of autophagy with other determinants of aging, is placing autophagy at the center of the aging process. In this work, we summarize evidence for the contribution of autophagy to health- and lifespan and provide examples of the bidirectional interplay between autophagic pathways and several of the so-called hallmarks of aging. This central role of autophagy in aging, and the dependence on autophagy of many geroprotective interventions, has motivated a search for direct modulators of autophagy that could be used to slow aging and extend healthspan. Here, we review some of those ongoing therapeutic efforts and comment on the potential of targeting autophagy in aging.

Acute kidney injury identification for pharmacoepidemiologic studies: Use of laboratory electronic acute kidney injury alerts versus electronic health records in Hospital Episode Statistics.

PURPOSE: A laboratory-based acute kidney injury (AKI) electronic-alert (e-alert) system, with e-alerts sent to the UK Renal Registry (UKRR) and collated in a master patient index (MPI), has recently been implemented in England. The aim of this study was to determine the degree of correspondence between the UKRR-MPI and AKI International Classification Disease-10 (ICD-10) N17 coding in Hospital Episode Statistics (HES) and whether hospital N17 coding correlated with 30-day mortality and emergency re-admission after AKI. METHODS: AKI e-alerts in people aged ≥18 years, collated in the UKRR-MPI during 2017, were linked to HES data to identify a hospitalised AKI population. Multivariable logistic regression was used to analyse associations between absence/presence of N17 codes and clinicodemographic features. Correlation of the percentage coded with N17 and 30-day mortality and emergency re-admission after AKI were calculated at hospital level. RESULTS: In 2017, there were 301 540 adult episodes of hospitalised AKI in England. AKI severity was positively associated with coding in HES, with a high degree of inter-hospital variability-AKI stage 1 mean of 48.2% [SD 14.0], versus AKI stage 3 mean of 83.3% [SD 7.3]. N17 coding in HES depended on demographic features, especially age (18-29 years vs. ≥85 years OR 0.22, 95% CI 0.21-0.23), as well as sex and ethnicity. There was no evidence of association between the proportion of episodes coded for AKI with short-term AKI outcomes. CONCLUSION: Coding of AKI in HES is influenced by many factors that result in an underestimation of AKI. Using e-alerts to triangulate the true incidence of AKI could provide a better understanding of the factors that affect hospital coding, potentially leading to improved coding, patient care and pharmacoepidemiologic research.

Natural products as geroprotectors: An autophagy perspective.

Over the past decade, significant attention has been given to repurposing Food and Drug Administration approved drugs to treat age-related diseases. In contrast, less consideration has been given to natural bioactive compounds. Consequently, there have been limited attempts to translate these compounds. Autophagy is a fundamental biological pathway linked to aging, and numerous strategies to enhance autophagy have been shown to extend lifespan. Interestingly, there are a number of natural products that are reported to modulate autophagy, and here we describe a number of them that activate autophagy through diverse molecular and cellular mechanisms. Among these, Urolithin A, Spermidine, Resveratrol, Fatty Acids and Phospholipids, Trehalose and Lithium are featured in detail. Finally, we outline possible strategies to optimise and increase the translatability of natural products, with the overall aim of delaying the ageing process and improving human healthspan.

A composite single-nucleotide polymorphism prediction signature for extranodal natural killer/T-cell lymphoma.

Current prognostic scoring systems based on clinicopathologic variables are inadequate in predicting the survival and treatment response of extranodal natural killer/T-cell lymphoma (ENKTL) patients undergoing nonanthracyline-based treatment. We aimed to construct a classifier based on single-nucleotide polymorphisms (SNPs) for improving predictive accuracy and guiding clinical decision making. Data from 722 patients with ENKTL from international centers were analyzed. A 7-SNP-based classifier was constructed using LASSO Cox regression in the training cohort (n = 336) and further validated in the internal testing cohort (n = 144) and in 2 external validation cohorts (n = 142 and n = 100). The 7-SNP-based classifier showed good prognostic predictive efficacy in the training cohort and the 3 validation cohorts. Patients with high- and low-risk scores calculated by the classifier exhibited significantly different progression-free survival (PFS) and overall survival (OS) (all P < .001). The 7-SNP-based classifier was further proved to be an independent prognostic factor by multivariate analysis, and its predictive accuracy was significantly better than clinicopathological risk variables. Application of the 7-SNP-based classifier was not affected by sample types. Notably, chemotherapy combined with radiotherapy significantly improved PFS and OS vs radiotherapy alone in high-risk Ann Arbor stage I patients, whereas there was no statistical difference between the 2 therapeutic modalities among low-risk patients. A nomogram was constructed comprising the classifier and clinicopathological variables; it showed remarkably better predictive accuracy than either variable alone. The 7-SNP-based classifier is a complement to existing risk-stratification systems in ENKTL, which could have significant implications for clinical decision making for patients with ENKTL.

A constant pool of Lgr5+ intestinal stem cells is required for intestinal homeostasis.

Lgr5+ crypt base columnar cells, the operational intestinal stem cells (ISCs), are thought to be dispensable for small intestinal (SI) homeostasis. Using a Lgr5-2A-DTR (diphtheria toxin receptor) model, which ablates Lgr5+ cells with near-complete efficiency and retains endogenous levels of Lgr5 expression, we show that persistent depletion of Lgr5+ ISCs in fact compromises SI epithelial integrity and reduces epithelial turnover in vivo. In vitro, Lgr5-2A-DTR SI organoids are unable to establish or survive when Lgr5+ ISCs are continuously eliminated by adding DT to the media. However, transient exposure to DT at the start of culture allows organoids to form, and the rate of outgrowth reduces with the increasing length of DT presence. Our results indicate that intestinal homeostasis requires a constant pool of Lgr5+ ISCs, which is supplied by rapidly reprogrammed non-Lgr5+ crypt populations when preexisting Lgr5+ ISCs are ablated.

CT texture analysis in histological classification of epithelial ovarian carcinoma.

OBJECTIVES: The study aimed to compare the ability of morphological and texture features derived from contrast-enhanced CT in histological subtyping of epithelial ovarian carcinoma (EOC). METHODS: Consecutive 205 patients with newly diagnosed EOC who underwent contrast-enhanced CT were included and dichotomised into high-grade serous carcinoma (HGSC) and non-HGSC. Clinical information including age and cancer antigen 125 (CA-125) was documented. The pre-treatment images were analysed using commercial software, TexRAD, by two independent radiologists. Eight qualitative CT morphological features were evaluated, and 36 CT texture features at 6 spatial scale factors (SSFs) were extracted per patient. Features' reduction was based on kappa score, intra-class correlation coefficient (ICC), univariate ROC analysis and Pearson's correlation test. Texture features with ICC ≥ 0.8 were compared by histological subtypes. Patients were randomly divided into training and testing sets by 8:2. Two random forest classifiers were determined and compared: model 1 incorporating selected morphological and clinical features and model 2 incorporating selected texture and clinical features. RESULTS: HGSC showed specifically higher texture features than non-HGSC (p < 0.05). Both models performed highly in predicting histological subtypes of EOC (model 1: AUC 0.891 and model 2: AUC 0.937), and no statistical significance was found between the two models (p = 0.464). CONCLUSION: CT texture analysis provides objective and quantitative metrics on tumour characteristics with HGSC demonstrating specifically high texture features. The model incorporating texture analysis could classify histology subtypes of EOC with high accuracy and performed as well as morphological features. KEY POINTS: • A number of CT morphological and texture features showed good inter- and intra-observer agreements. • High-grade serous ovarian carcinoma showed specifically higher CT texture features than non-high-grade serous ovarian carcinoma. • CT texture analysis could differentiate histological subtypes of epithelial ovarian carcinoma with high accuracy.

The haemoglobinopathy survey: The reality of transfusion practice in sickle cell disease and thalassaemia in England.

OBJECTIVES: To establish, in an unselected population of London haemoglobinopathy patients, transfusion requirements, blood antigens/alloantibodies, transfusion modalities, burden of transfusion reactions and donor exposure. BACKGROUND: Haemoglobinopathy patients are among the most highly transfused patient populations, and the overall population and number of patients on long-term transfusion programmes are increasing. To provide a safe and efficacious transfusion service for patients, it is important to understand current practice, morbidity associated with transfusion, efficacy of different transfusion modalities and geno-/phenotype requirements. METHODS: Data on 4451 transfusion episodes in 760 patients from 12 London hospitals were collected retrospectively over a 6-month period in 2011. RESULTS: Alloimmunisation prevalence was 17% for sickle cell disease (SCD) and 22% for thalassaemia, most commonly anti-Rh/Kell/Kpa /Cw . Rh phenotypes differed between SCD (Ro r 59.8%/R1 r 15.9%/R2 r 15.6%) and thalassaemia (R1 R1 29.6%/R1 r 28.4%/R1 R2 15.4%). Recording of pheno-/genotypes fell below recommendations. A 2-weekly manual exchange and 3-weekly automated exchange came closest to achieving presumptive targets. In adults with thalassaemia, the mean blood requirement was 36 units per year; for SCD, erythrocytapheresis was carried out every 7 weeks with 66 units; for manual exchange, it was 38 units every 4 weeks; and for simple transfusion, it was 30 units p.a. every 4 weeks. CONCLUSION: Transfusion modality choice was influenced by the resources available-children mostly received simple transfusions, and adults received erythrocytapheresis; the relationships between frequency of exchanges/transfusion modality/target HbA% were not simple, possibly reflecting the difference in recipient erythropoiesis and consequent transfusion modality selection bias; adherence to existing and current guidelines regarding geno-/phenotyping was limited; and alloimmunisation had a low incidence and high prevalence in both disorders.

Sociodemographic features and mortality of individuals on haemodialysis treatment who test positive for SARS-CoV-2: A UK Renal Registry data analysis.

Kidney disease is a recognised risk factor for poor COVID-19 outcomes. Up to 30 June 2020, the UK Renal Registry (UKRR) collected data for 2,385 in-centre haemodialysis (ICHD) patients with COVID-19 in England and Wales. Overall unadjusted survival at 1 week after date of positive COVID-19 test was 87.5% (95% CI 86.1-88.8%); mortality increased with age, treatment vintage and there was borderline evidence of Asian ethnicity (HR 1.16, 95% CI 0.94-1.44) being associated with higher mortality. Compared to the general population, the relative risk of mortality for ICHD patients with COVID-19 was 45.4 and highest in younger adults. This retrospective cohort study based on UKRR data supports efforts to protect this vulnerable patient group.