Impact of gender, ethnicity and social deprivation on access to surgical or transcatheter aortic valve replacement in aortic stenosis: a retrospective database study in England.

OBJECTIVE: To assess gender, ethnicity, and deprivation-based differences in provision of aortic valve replacement (AVR) in England for adults with aortic stenosis (AS). METHODS: We retrospectively identified adults with AS from the English Hospital Episode Statistics (HES) between April 2016 and March 2019 and those who subsequently had an AVR. We separately used HES-linked Clinical Practice Research Datalink (CPRD) to identify people with AVR and evaluate the timeliness of their procedure (CPRD-AVR cohort). ORs for AVR in people with an AS diagnosis were estimated using multivariable logistic regression adjusted for age, region and comorbidity. AVR was considered timely if performed electively and without evidence of cardiac decompensation before AVR. RESULTS: 183 591 adults with AS were identified in HES; of these, 31 436 underwent AVR. The CPRD-AVR cohort comprised 10 069 adults. Women had lower odds of receiving AVR compared with men (OR 0.65; 95% CI 0.63 to 0.66); as did people of black (OR 0.70; 95% CI 0.60 to 0.82) or South Asian (OR 0.75; 95% CI 0.69 to 0.82) compared with people of white ethnicities. People in the most deprived areas were less likely to receive AVR than the least deprived areas (OR 0.8; 95% CI 0.75 to 0.86). Timely AVR occurred in 65% of those of white ethnicities compared with 55% of both those of black and South Asian ethnicities. 77% of the least deprived had a timely procedure compared with 58% of the most deprived; there was no gender difference. CONCLUSIONS: In this large, national dataset, female gender, black or South Asian ethnicities and high deprivation were associated with significantly reduced odds of receiving AVR in England. A lower proportion of people of minority ethnicities or high deprivation had a timely procedure. Public health initiatives may be required to increase clinician and public awareness of unconscious biases towards minority and vulnerable populations to ensure timely AVR for everyone.

Gene-Transcript Expression in Urine Supernatant and Urine Cell-Sediment Are Different but Equally Useful for Detecting Prostate Cancer.

There is considerable interest in urine as a non-invasive liquid biopsy to detect prostate cancer (PCa). PCa-specific transcripts such as the TMPRSS2:ERG fusion gene can be found in both urine extracellular vesicles (EVs) and urine cell-sediment (Cell) but the relative usefulness of these and other genes in each fraction in PCa detection has not been fully elucidated. Urine samples from 76 men (PCa n = 40, non-cancer n = 36) were analysed by NanoString for 154 PCa-associated genes-probes, 11 tissue-specific, and six housekeeping. Comparison to qRT-PCR data for four genes (PCA3, OR51E2, FOLH1, and RPLP2) was strong (r = 0.51-0.95, Spearman p < 0.00001). Comparing EV to Cells, differential gene expression analysis found 57 gene-probes significantly more highly expressed in 100 ng of amplified cDNA products from the EV fraction, and 26 in Cells (p < 0.05; edgeR). Expression levels of prostate-specific genes (KLK2, KLK3) measured were ~20× higher in EVs, while PTPRC (white-blood Cells) was ~1000× higher in Cells. Boruta analysis identified 11 gene-probes as useful in detecting PCa: two were useful in both fractions (PCA3, HOXC6), five in EVs alone (GJB1, RPS10, TMPRSS2:ERG, ERG_Exons_4-5, HPN) and four from Cell (ERG_Exons_6-7, OR51E2, SPINK1, IMPDH2), suggesting that it is beneficial to fractionate whole urine prior to analysis. The five housekeeping genes were not significantly differentially expressed between PCa and non-cancer samples. Expression signatures from Cell, EV and combined data did not show evidence for one fraction providing superior information over the other.

A Model to Detect Significant Prostate Cancer Integrating Urinary Peptide and Extracellular Vesicle RNA Data.

There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: 'MassSpec' (CE-MS proteomics), 'EV-RNA', and 'SoC' (standard of care) clinical data models, alongside a fully integrated omics-model, deemed 'ExoSpec'. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77−0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1−3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.