Model based on four independent variables.

ObjectiveTo identify survival-related risk factors in patients with advanced schistosomiasis, develop a predictive model using integrative approach of LASSO-Cox regression, and construct a nomogram for visualizing the model’s risk prediction framework.MethodsData from 628 advanced schistosomiasis patients treated at Dongzhi Schistosomiasis Hospital between 2019 and 2022 were retrospectively analyzed. LASSO regression was used to select variables associated with survival outcomes, which were subsequently incorporated into a Cox proportional hazards (CPH) model. Internal validation included assessments of discriminative ability (C-index, area under the receiver operating characteristic curve [AUC]), calibration (calibration curves), and clinical utility (decision curve analysis) to evaluate model performance. The final model was visualized via a nomogram depicting the risk prediction algorithm.ResultsLASSO regression identified four independent predictors: carbohydrate antigen 125, hyaluronic acid, ascites grade Ⅱ, and ascites grade Ⅲ. The LASSO-Cox model exhibited strong discriminative performance, with a C-index of 0.886 (SE = 0.025) in the training set and 0.922 (SE = 0.025) in the validation set. Calibration curves showed excellent agreement between predicted and observed survival probabilities, and decision curve analysis confirmed clinical utility across a range of threshold probabilities. A nomogram was developed to translate the model into a user-friendly visual tool for risk stratification.ConclusionsThe constructed nomogram serves as a practical tool for identifying advanced schistosomiasis patients at high mortality risk. Clinicians can leverage this model to tailor individualized follow-up and treatment strategies, potentially improving long-term outcomes by targeting interventions to patients with the greatest need.