Background: Frailty is a known predictor of outcome and mortality in patients undergoing liver transplantation. However, most patients remain unsuitable transplant candidates. It is not yet known if the assessment of frailty in non-transplant candidates can aid prognostication. Aim: To collate and interrogate the various frailty tools presently used to predict mortality in the non-transplant cirrhosis setting. Methods: A comprehensive review of MEDLINE and EMBASE databases for articles published from inception to March 2022 was undertaken, excluding those where patients underwent transplantation or had hepatocellular carcinoma.Results: We identified 12 observational cohort studies, featuring 9 frailty indices.These were from various global healthcare settings and of fair or good quality. Most were objective tools utilising clinician-based assessments. All frailty scores predicted prognosis, with variability in the method of application, and utilisation in long-or short-term mortality. Three studies directly compared different indices in the same population. There was some evidence that simple tools could perform as well, if not better, than more complex, time-consuming scores.Conclusions: Various frailty tools can reproducibly evaluate mortality in patients with cirrhosis who are ineligible for transplant. However, further prospective headto-head comparative studies are needed. In addition to determining model utility, studies should focus on important relative considerations which may limit widespread implementation including, ease of use and limited resources, given the global disparity of liver care provision. These tools may positively identify specific patient cohorts at risk of impending deterioration, thereby stratifying those patients likely to benefit from early integration with palliative care.
3034 Background: A rapid, low-cost, sensitive, multi-cancer early detection (MCED) test would be transformational in the diagnostics field. Earlier cancer detection and instigation of treatment can increase survival rates. An effective test must accurately identify the small proportion of patients with typically non-specific symptoms who actually have cancer. Such symptoms don’t easily segregate by organ system, necessitating a multi-cancer approach. Methods: In this large-scale study ( n = 2094 patients) we applied the Dxcover Cancer Liquid Biopsy to differentiate cancer against non-cancer, as well as organ specific tests to identify cancers of the brain, breast, colorectal, kidney, lung, ovary, pancreas, and prostate. The test uses Fourier transform infrared spectroscopy to analyze all macromolecules in a minute volume of patient serum, and machine learning to build a classifier of the resultant spectral profiles for calling the likelihood of cancer. Results: For the overall cancer classification, our model achieved 90% sensitivity with 61% specificity when tuned for sensitivity, with detection rates of 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV. We also tuned for maximum sensitivity or specificity, whilst the other statistic was fixed above a minimum value of 45%. This resulted in 94% sensitivity with 47% specificity, and 94% specificity with 48% sensitivity, respectively. For organ specific cancer classifiers area under the curve values were calculated for all cancers: brain (0.90), breast (0.74), colorectal (0.91), kidney (0.91), lung (0.90), ovarian (0.85), pancreatic (0.81) and prostate (0.85). Conclusions: Cancer treatment is often more effective when given earlier and this low-cost strategy can facilitate the requisite earlier diagnosis. With further development, the Dxcover MCED test could have a significant impact on early detection of cancer, which is vital in the quest for improved survival and quality of life.
ObjectiveThe increasing prevalence of liver disease in the UK means there is a pressing need to expand the hepatology workforce. This survey aims to evaluate current hepatology training provision, and trainee attitudes towards future careers in hepatology.MethodAn electronic survey was distributed to higher specialty gastroenterology and hepatology trainees in the UK between March and May 2022.Results138 trainees completed the survey covering all training grades and regions of the UK. 73.7% reported receiving adequate hepatology training currently, with 55.6% intending to become future hepatologists. Trainee preference for future hepatology consultant posts in specialist liver centres were almost threefold higher compared with district general hospitals (60.9% vs 22.6%). All trainees, irrespective of training grade reported high confidence in managing decompensated cirrhosis in both inpatient and outpatient settings. Senior trainees (grade ST6 and higher), without advanced training programme (ATP) experience reported significantly lower confidence in managing viral hepatitis, hepatocellular carcinoma and post-transplant patients compared with equivalent trainees with ATP experience. For junior trainees (IMT3–ST5), remaining in their current deanery was the most important factor when considering future hepatology training application.ConclusionsThere is a significant need to deliver widely available training on the management of complex liver disease to improve non-ATP trainee confidence. Innovative job planning strategies are required to encourage trainees to pursue careers outside of specialist liver centres. Expansion of hepatology training networks with wider geographical coverage are needed to address the growing need for more hepatologists around the UK.
BACKGROUND Brain tumour patients have the highest stroke mortality rates among cancer types, but the factors associated with fatal stroke remain unknown. We aimed to examine to what extent brain tumour grade, a marker of biological aggressiveness, tumour size and cancer treatment are associated with cerebrovascular mortality among patients with malignant glioma, the most common and aggressive brain tumour. METHODS We conducted a retrospective, observational cohort study using the US National Cancer Institute’s state and regional population-based cancer registries (NCI SEER). We identified adult patients with a diagnosis of malignant glioma (2000 to 2018, Nf72,916). The primary outcome of interest was death from cerebrovascular disease. Cox regression modelling estimated the associations with cerebrovascular mortality of tumour grade, tumour size and treatment (surgery, radiotherapy, chemotherapy), calculating hazard ratios (HR) adjusted for these factors as well as for age, sex, race, marital status and calendar year. RESULTS Higher grade (Grade IV vs Grade II: HR=2.47, 95% CI=1.69-3.61, p< 0.001) and larger brain tumours (size 3 to < 6 cm: HR=1.40, 95% CI=1.03 -1.89, p< 0.05; size ≥ 6 cm: HR=1.47, 95% CI=1.02-2.13, p< 0.05 compared to size < 3cm) were associated with increased cerebrovascular mortality. Having cancer treatment was associated with decreased risk (surgery: HR= 0.60, p< 0.001; chemotherapy: HR=0.42, p< 0.001; radiation: HR= 0.69, p< 0.05). However, among patents surviving five years or more from their cancer diagnosis radiotherapy was associated with higher risk of cerebrovascular mortality (HR 2.73, 95% CI 1.49-4.99, p< 0.01). CONCLUSIONS More aggressive tumour characteristics are associated with increased cerebrovascular mortality, and treatment was associated with lower risk within 5 years of diagnosis. Radiotherapy increased risk of fatal cerebrovascular outcome five-years after cancer diagnosis. Further research is needed to better understand the long-term cardiovascular consequences of radiation therapy, and whether the consequent risk can be mitigated.
Mapping the molecular composition of tissues using spatial biology provides high-content information for molecular diagnostics. However, spatial biology approaches require invasive procedures to collect samples and destroy the investigated tissue, limiting the extent of analysis, particularly for highly functional tissues such as those of the brain. To address these limitations, we developed a workflow to harvest biomolecules from brain tissues using nanoneedles and characterise the distribution of lipids using desorption electrospray ionization mass spectrometry imaging. The nanoneedles preserved the original tissue while harvesting a reliable molecular profile and retaining the original lipid distribution for mouse and human brain samples, accurately outlining the morphology of key regions within the brain and tumour lesions. The deep neural network analysis of a cohort containing 23 human glioma biopsies showed that nanoneedle samples maintain the molecular signatures required to accurately classify disease state. Thus, nanoneedles provide a route for tissue-preserving spatial lipidomic and molecular diagnostics.
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