Genetic Ancestry, Race, and Severity of Acutely Decompensated Cirrhosis in Latin America.

BACKGROUND & AIMS: Genetic ancestry or racial differences in health outcomes exist in diseases associated with systemic inflammation (eg, COVID-19). This study aimed to investigate the association of genetic ancestry and race with acute-on-chronic liver failure (ACLF), which is characterized by acute systemic inflammation, multi-organ failure, and high risk of short-term death. METHODS: This prospective cohort study analyzed a comprehensive set of data, including genetic ancestry and race among several others, in 1274 patients with acutely decompensated cirrhosis who were nonelectively admitted to 44 hospitals from 7 Latin American countries. RESULTS: Three hundred ninety-five patients (31.0%) had ACLF of any grade at enrollment. Patients with ACLF had a higher median percentage of Native American genetic ancestry and lower median percentage of European ancestry than patients without ACLF (22.6% vs 12.9% and 53.4% vs 59.6%, respectively). The median percentage of African genetic ancestry was low among patients with ACLF and among those without ACLF. In terms of race, a higher percentage of patients with ACLF than patients without ACLF were Native American and a lower percentage of patients with ACLF than patients without ACLF were European American or African American. In multivariable analyses that adjusted for differences in sociodemographic and clinical characteristics, the odds ratio for ACLF at enrollment was 1.08 (95% CI, 1.03-1.13) with Native American genetic ancestry and 2.57 (95% CI, 1.84-3.58) for Native American race vs European American race CONCLUSIONS: In a large cohort of Latin American patients with acutely decompensated cirrhosis, increasing percentages of Native American ancestry and Native American race were factors independently associated with ACLF at enrollment.

A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis.

BACKGROUND: Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis. METHODS: To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based). RESULTS: Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580). CONCLUSIONS: By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.