Hepatitis, a leading global health challenge, contributes to over 1.3 million deaths annually, with hepatitis B and C accounting for the majority of these fatalities. Intensive care unit (ICU) management of patients is particularly challenging due to the complex clinical care and resource demands. This study focuses on predicting Length of Stay (LoS) and discharge outcomes for ICU-admitted hepatitis patients using machine learning models. Despite advancements in ICU predictive analytics, limited research has specifically addressed hepatitis patients, creating a gap in optimizing care for this population. Leveraging data from the MIMIC-IV database, which includes around 94,500 ICU patient records, this study uses sociodemographic details, clinical characteristics, and resource utilization metrics to develop predictive models. Using Random Forest, Logistic Regression, Gradient Boosting Machines, and Generalized Additive Model with Negative Binomial Regression, these models identified medications, procedures, comorbidities, age, and race as key predictors. Total LoS emerged as a pivotal factor in predicting discharge outcomes and location. These findings provide actionable insights to improve resource allocation, enhance clinical decision-making, and inform future ICU management strategies for hepatitis patients.

The authors have declared no competing interest.

This study did not receive any funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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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confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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.

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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).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The datasets used for this study are available at MIMIC-IV v3.0 only for credentialed users following the PhysioNet Credentialed Health Data Use Agreement (DUA).

https://physionet.org/content/mimiciv/3.0/