Background Emerging evidence suggests ethnic minorities are disproportionately affected by COVID-19. Detailed clinical analyses of multi-cultural hospitalized patient cohorts remain largely undescribed. Methods We performed regression, survival and cumulative competing risk analyses to evaluate factors associated with mortality in patients admitted for COVID-19 in three large London hospitals between February 25 and April 5, censored as of May 1, 2020. Results Of 614 patients (median age 69 years, (IQR 25) and 62% male), 381 (62%) had been discharged alive, 178 (29%) died and 55 (9%) remained hospitalized at censoring. Severe hypoxemia (aOR 4.25, 95%CI 2.36-7.64), leukocytosis (aOR 2.35, 95%CI 1.35-4.11), thrombocytopenia (aOR 1.01, 95%CI 1.00-1.01, increase per 10x9 decrease), severe renal impairment (aOR 5.14, 95%CI 2.65-9.97), and low albumin (aOR 1.06, 95%CI 1.02-1.09, increase per g decrease) were associated with death. Forty percent (244) were from black, Asian and other minority ethnic (BAME) groups, 38% (235) white and for 22% (135) ethnicity was unknown. BAME patients were younger and had fewer comorbidities. Whilst the unadjusted odds of death did not differ by ethnicity, when adjusting for age, sex and comorbidities, black patients were at higher odds of death compared to whites (aOR 1.69, 95%CI 1.00-2.86). This association was stronger when further adjusting for admission severity (aOR 1.85 95% CI 1.06-3.24). Conclusions BAME patients were over-represented in our cohort and, when accounting for demographic and clinical profile of admission, black patients were at increased odds of death. Further research is needed into biologic drivers of differences in COVID-19 outcomes by ethnicity.

Background & Aims Liver biopsy is the reference standard for staging and grading nonalcoholic fatty liver disease (NAFLD), but histologic scoring systems are semiquantitative with marked interobserver and intraobserver variation. We used machine learning to develop fully automated software for quantification of steatosis, inflammation, ballooning, and fibrosis in biopsy specimens from patients with NAFLD and validated the technology in a separate group of patients. Methods We collected data from 246 consecutive patients with biopsy-proven NAFLD and followed up in London from January 2010 through December 2016. Biopsy specimens from the first 100 patients were used to derive the algorithm and biopsy specimens from the following 146 were used to validate it. Biopsy specimens were scored independently by pathologists using the Nonalcoholic Steatohepatitis Clinical Research Network criteria and digitalized. Areas of steatosis, inflammation, ballooning, and fibrosis were annotated on biopsy specimens by 2 hepatobiliary histopathologists to facilitate machine learning. Images of biopsies from the derivation and validation sets then were analyzed by the algorithm to compute percentages of fat, inflammation, ballooning, and fibrosis, as well as the collagen proportionate area, and compared with findings from pathologists’ manual annotations and conventional scoring systems. Results In the derivation group, results from manual annotation and the software had an interclass correlation coefficient (ICC) of 0.97 for steatosis (95% CI, 0.95–0.99; P < .001); ICC of 0.96 for inflammation (95% CI, 0.9–0.98; P < .001); ICC of 0.94 for ballooning (95% CI, 0.87–0.98; P < .001); and ICC of 0.92 for fibrosis (95% CI, 0.88–0.96; P = .001). Percentages of fat, inflammation, ballooning, and the collagen proportionate area from the derivation group were confirmed in the validation cohort. The software identified histologic features of NAFLD with levels of interobserver and intraobserver agreement ranging from 0.95 to 0.99; this value was higher than that of semiquantitative scoring systems, which ranged from 0.58 to 0.88. In a subgroup of paired liver biopsy specimens, quantitative analysis was more sensitive in detecting differences compared with the nonalcoholic steatohepatitis Clinical Research Network scoring system. Conclusions We used machine learning to develop software to rapidly and objectively analyze liver biopsy specimens for histologic features of NAFLD. The results from the software correlate with those from histopathologists, with high levels of interobserver and intraobserver agreement. Findings were validated in a separate group of patients. This tool might be used for objective assessment of response to therapy for NAFLD in practice and clinical trials.

Background & aims Although metabolic risk factors are associated with more severe COVID-19, there is little evidence on outcomes in patients with non-alcoholic fatty liver disease (NAFLD). We here describe the clinical characteristics and outcomes of NAFLD patients in a cohort hospitalised for COVID-19. Methods This study included all consecutive patients admitted for COVID-19 between February and April 2020 at Imperial College Healthcare NHS Trust, with either imaging of the liver available dated within one year from the admission or a known diagnosis of NAFLD. Clinical data and early weaning score (EWS) were recorded. NAFLD diagnosis was based on imaging or past medical history and patients were stratified for Fibrosis-4 (FIB-4) index. Clinical endpoints were admission to intensive care unit (ICU)and in-hospital mortality. Results 561 patients were admitted. Overall, 193 patients were included in the study. Fifty nine patients (30%) died, 9 (5%) were still in hospital, and 125 (65%) were discharged. The NAFLD cohort (n = 61) was significantly younger (60 vs 70.5 years, p = 0.046) at presentation compared to the non-NAFLD (n = 132). NAFLD diagnosis was not associated with adverse outcomes. However, the NAFLD group had higher C reactive protein (CRP) (107 vs 91.2 mg/L, p = 0.05) compared to non-NAFLD(n = 132). Among NAFLD patients, male gender (p = 0.01), ferritin (p = 0.003) and EWS (p = 0.047) were associated with in-hospital mortality, while the presence of intermediate/high risk FIB-4 or liver cirrhosis was not.

Summary Background Atherosclerotic cardiovascular disease is a key cause of morbidity in non‐alcoholic fatty liver disease (NAFLD) but appropriate means to predict major acute cardiovascular events (MACE) are lacking. Aim To design a bespoke cardiovascular risk score in NAFLD. Methods A retrospective derivation (2008‐2016, 356 patients) and a prospective validation (2016‐ 2017, 111 patients) NAFLD cohort study was performed. Clinical and biochemical data were recorded at enrolment and mean platelet volume (MPV), Qrisk2 and Framingham scores were recorded one year prior to MACE (Cardiovascular death, acute coronary syndrome, stroke and transient ischaemic attack). Results The derivation and validation cohorts were well‐matched, with MACE prevalence 12.6% and 12%, respectively. On univariate analysis, age, diabetes, advanced fibrosis, collagen proportionate area >5%, MPV and liver stiffness were associated with MACE. After multivariate analysis, age, diabetes and MPV remained independently predictive of MACE. The “NAFLD CV‐risk score” was generated using binary logistic regression: 0.06*(Age) + 0.963*(MPV) + 0.26*(DM1) – 16.44; 1Diabetes mellitus: 1: present; 2: absent. (AUROC 0.84). A cut‐off of −3.98 gave a sensitivity 97%, specificity 27%, PPV 16%, and NPV 99%. An MPV alone of >10.05 gave a sensitivity 97%, specificity 59%, PPV 24% and NPV 97% (AUROC 0.83). Validation cohort AUROCs were comparable at 0.77 (NAFLD CV‐risk) and 0.72 (MPV). In the full cohort, the NAFLD CV‐risk score and MPV outperformed both Qrisk2 and Framingham scores. Conclusions The NAFLD CV risk score and MPV accurately predict 1‐year risk of MACE, thereby allowing better identification of patients that require optimisation of their cardiovascular risk profile.