# Prognostic determinants and mortality risk of advanced schistosomiasis revealed by Lasso-Cox regression integrative approach

## TL;DR

This study identifies four risk factors for mortality in advanced schistosomiasis and creates a nomogram to help doctors assess patient risk and improve treatment decisions.

## Contribution

A novel Lasso-Cox regression integrative approach was used to develop a predictive nomogram for mortality risk in advanced schistosomiasis.

## Key findings

- Four independent predictors of mortality were identified: carbohydrate antigen 125, hyaluronic acid, ascites grade II, and ascites grade III.
- The LASSO-Cox model showed strong discriminative performance with a C-index of 0.886 in training and 0.922 in validation.
- A nomogram was developed and validated as a practical tool for risk stratification in clinical settings.

## Abstract

To 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.

Data 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.

LASSO 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.

The 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.

Human schistosomiasis is a neglected tropical disease caused by trematode parasites of the genus Schistosoma. It is particularly dangerous for people who live in regions featured by inadequate access to sanitation and safe water, and predicting its severity is critical for timely intervention. This study aimed to develop a simple, user-friendly tool to survival-related risk factors in patients with advanced schistosomiasis. Through a retrospective analysis of clinical data from 628 patients with advanced schistosomiasis, we identified four critical factors- carbohydrate antigen 125, hyaluronic acid, ascites grade Ⅱ, and ascites grade Ⅲ, that can predict disease severity. Based on these four factors, we developed a nomogram tool to assist clinicians in quickly assessing the risk of patients with advanced schistosomiasis. The validation of the nomogram demonstrated its accuracy and utility in clinical decision-making. Our study suggests that this nomogram could serve as a valuable tool in healthcare, aiding physicians in identifying patients with advanced schistosomiasis of high mortality risk accurately, making more informed treatment decisions, and provide guidance for health administrative department to make management decisions to decrease the disease burden.