ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

Patient heterogeneity represents a significant challenge for both individual patient management and clinical trial design, especially in the context of complex diseases. Most existing clinical classifications are based on scores built to predict the outcomes of the patients. These classical methods may thus miss features that contribute to heterogeneity without necessarily translating into prognostic implications. To address patient heterogeneity at hospital admission, we developed ClustALL, a computational pipeline designed to handle common clinical data challenges such as mixed data types, missing values, and collinearity. ClustALL also facilitates the unsupervised identification of multiple and robust stratifications. We applied ClustALL to a prospective European multicentre cohort of patients with acutely decompensated cirrhosis (AD) (n=766), a highly heterogeneous disease. ClustALL identified five robust stratifications for patients with AD, 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 but also having a prognostic value. Re-assessment of patient stratification during follow-up delineated the outcomes of the patients, 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). In conclusion, this study developed ClustALL, a novel and robust stratification method capable of addressing challenges tied to intricate clinical data and applicable to complex diseases. By applying ClustALL to patients with AD, we identified three patient clusters, offering insights that could guide future clinical trial design. ### Competing Interest Statement Jonel Trebicka has received speaking and/or consulting fees from Versantis, Gore, Boehringer-Ingelheim, Falk, Grifols, Genfit and CSL Behring. ### Funding Statement This project has received funding from the European Union Horizon 2020 research and innovation program under grant agreement No 847949. The study was supported by the European Foundation for the Study of Chronic Liver Failure (EF-Clif). The EF-Clif is a nonprofit private organization. The EF-Clif receives unrestricted donations from Cellex Foundation and Grifols. EF-Clif is partner, contributor and coordinator in several EU Horizon 2020 program projects. JT was appointed as visiting Professor in EF-Clif for the execution of the study by a grant from Cellex Foundation. The funders had no influence on study design, data collection and analysis, decision to publish or preparation of the manuscript.e fact that EF-CLIF. N.P.P was funded by a Ramon y Cajal fellow (RYC2021‐032197‐I) from the MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR and by a Juan de la Cierva-formacion fellow (FJC2019-042304-I) from the Spanish Ministry of Science and Innovation (MCIN). P-E.R. research laboratory is supported by the Foundation pour la Recherche Medicale (FRM EQU202303016287), Institut National de la Sante et de la Recherche Medicale (ATIP AVENIR), the Agence Nationale pour la Recherche (ANR-18-CE14-0006-01, RHU QUID-NASH, ANR-18-IDEX-0001, ANR-22-CE14-0002) by Emergence, Ville de Paris , by Fondation ARC and by the European Union Horizon 2020 research and innovation programme under grant agreement No 847949. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This project uses data derived from the following studies: PREDICT Study: 10.1016/j.jhep.2020.06.013, ([NCT03056612][1]) ACLARA Study: 10.1053/j.gastro.2023.05.033 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Researchers who provide a methodology sound proposal can apply for the data, as far as the proposal is in line with the research consented by the patients. These proposals should be requested through https://www.clifresearch.com/decision/Home.aspx Data requestors will need to sign a data transfer agreement. The code to generate the ClustALL method is available on GitHub, at https://github.com/TranslationalBioinformaticsUnit/ClustALL_AD. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03056612&atom=%2Fmedrxiv%2Fearly%2F2023%2F11%2F18%2F2023.11.17.23298672.atom