Optimise diuretic therapy in cirrhotic care: predictive model development for enhanced fluid management for better outcomes

Objectives: Diuretic therapy in liver cirrhosis is traditionally adjusted through a trial-and-error process, characterised by inefficiencies, frequent therapy modifications, prolonged hospital stays and high economic costs. Predictive analytics powered by machine learning (ML) offers a transformative solution, enabling personalised and precise interventions that minimise regimen changes, expedite recovery and enhance healthcare efficiency. This study evaluates the impact of ML-guided diuretic therapy on reducing treatment modifications, hospitalisations and associated costs, ultimately delivering precision-driven outcomes. Materials and Methods: An ambidirectional cohort study analysed 481 cirrhotic patients receiving diuretic therapy. Key clinical data, including liver function markers, serum electrolytes and urinary output, were collected to analyse therapy modifications and identify predictors of suboptimal outcomes. ML classification models were trained to predict optimal diuretic regimens using one-hot encoding for categorical variables. Model performance was assessed through the area under the curve, accuracy, sensitivity and specificity. Hyperparameter optimisation enhanced predictive accuracy, while feature importance analysis identified critical predictors of diuretic response. Results: Under the conventional approach, 71.5% of patients initially received suboptimal therapy, leading to add-on treatments (44%) or complete regimen changes (27%). The ML model reduced therapy modifications by 58%, achieving 81% accuracy in predicting optimal regimens. Key predictors of diuretic response included serum albumin, bilirubin and creatinine levels. Incorporating ML reduced the time to optimal therapy from 7–8 days to 4 days, shortened hospital stays by 40% and significantly lowered healthcare costs. Conclusion: ML-guided diuretic management in liver cirrhosis represents a paradigm shift, delivering precision-driven care that minimises therapy changes, shortens hospital stays and reduces economic burden. By leveraging key clinical predictors, this approach accelerates recovery and enhances treatment precision. Further validation in larger datasets and real-world settings is critical to establishing ML as a cornerstone of fluid management in cirrhosis, enabling cost-effective, patient-centred care.