Background and Aims Hepatorenal syndrome - Acute Kidney Injury (HRS-AKI) is a severe complication of decompensated cirrhosis that is challenging to predict. Sentiment analysis, a computational process of identifying and categorizing opinions and judgment expressed in text, may enhance traditional prediction methodologies based on structured variables. Large language models (LLMs), such as generative pre-trained transformers (GPTs), have demonstrated abilities to perform sentiment analyses on non-clinical texts. We sought to determine if GPT-performed sentiment analysis could improve upon predictions using clinical covariates alone in the prediction of HRS-AKI. Methods Adult patients admitted to a single academic medical center with decompensated cirrhosis and AKI. We used a protected health information (PHI) compliant version of Microsoft Azure OpenAI GPT-4o to derive a sentiment score ranging from 0 to 1 for HRS-AKI, and conduct natural language processing (NLP) extraction of clinical terms associated with HRS-AKI in clinical notes. The area under the receiver operator curve (AUROC) was compared in logistic regression models incorporating structured variables (socio-demographics, MELD 3.0, hemodynamic parameters) with compared to without sentiment scores and NLP-extracted clinical terms. Results In our cohort of 314 participants, higher sentiment score was associated with the diagnosis of HRS-AKI (OR 1.33 per 0.1, 95% CI 1.02-1.79) in multivariate models. AUROC of the baseline model using structured clinical covariates alone was 0.639. With the addition of the GPT-4o derived sentiment score and clinical terms to structured covariates, the final model yielded an improved AUROC of 0.758 (p= 0.03). Conclusions Clinical texts contain large amounts of data that are currently difficult to extract using standard methodologies. Sentiment analysis and NLP-based variable derivation with GPT-4o in clinical application is feasible and can improve the prediction of HRS-AKI over traditional modeling methodologies alone.

- Dr. Jennifer C. Lai receives research support from Lipocene and Vir Biotechnologies; receives an education grant from Nestle Nutrition Sciences; serves on an advisory board for Novo Nordisk; and consults for Genfit, Third Rock Ventures, and Boehringer Ingelheim. - Dr. Jin Ge receives research support from Merck and Co; and consults for Astellas Pharmaceuticals/Iota Biosciences. - Dr. Giuseppe Cullaro consults for Ocelot Bio and Retro Biosciences.

K23DK135901 (National Institute of Diabetes and Digestive and Kidney Diseases; Rubin), P30DK026743 (UCSF Liver Center Grant; Rubin; Huang; Lai, J; and Ge), UL1TR001872 (National Center for Advancing Translational Sciences; Pletcher), R01AG059183/K24AG080021 (National Institute on Aging; Lai, J), K23DK131278/L30DK133959 (National Institute of Diabetes and Digestive and Kidney Disease;, Cullaro), K23DK139455 (National Institute of Diabetes and Digestive and Kidney Diseases, Ge). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or any other funding agencies. The funding agencies played no role in the analysis of the data or the preparation of this manuscript.

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This study was approved by the UCSF IRB under IRB # 11-07513.

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