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. 2025 Mar 15;17(3):1901-1909.
doi: 10.62347/XXIL7895. eCollection 2025.

Construction and validation of a nomogram model to predict mortality risk in patients with acute diquat poisoning

Lu Jiang  1   2 , Mei Li  3 , Yinglian Fang  4 , Xue Yang  5   6
Affiliations

Construction and validation of a nomogram model to predict mortality risk in patients with acute diquat poisoning

Lu Jiang et al. Am J Transl Res. .

Abstract

Objective: To analyze the risk factors for mortality in patients with acute diquat (DQ) poisoning and construct a nomogram prediction model for clinical assessment and treatment.

Methods: A retrospective analysis was performed on the clinical data of 110 patients with acute DQ poisoning who were admitted from March 2022 to April 2024. The enrolled patients were divided into a training set of 80 cases and a validation set of 30 cases. A survival group and a death group were established, with death within 30 days as the endpoint. Among these, in the training group, there were 67 cases in the survival group and 13 cases in the death group. This study further analyzed and compared the baseline and clinical data of the two groups of patients, screened potential risk factors using Least absolute shrinkage and selection operator (LASSO) regression, and determined independent risk factors through multivariate logistic regression analysis. A nomogram predictive model was constructed and validated based on the validation set.

Results: Using LASSO regression, this study screened 13 possible risk factors. The dosage of DQ, gastric lavage rate, medication to hospital admission time, alanine aminotransferase, aspartate aminotransferase, blood potassium, creatinine, urea, partial pressure of oxygen, urinary DQ concentration, Systemic Inflammatory Response Syndrome (SIRS) score, Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology and Chronic Health Evaluation (APACHE) II score were found to predict death significantly after acute DQ poisoning. This study further constructed the nomogram predictive model and validated the predictive performance of this model by using a validation set. The Area Under the Curve (AUC) of the training set was 0.961, and that of the validation set was 0.947. The calibration curve of the training and validation sets showed good prediction results of the model, and the calibration curve tended to approach the ideal curve.

Conclusion: This study constructed a nomogram model to predict mortality risk in patients with acute DQ poisoning. Clinicians will have a clearer and intuitive understanding of the prognosis of patients, so as to enhance the treatment of patients and optimize the allocation of medical resources.

Keywords: Diquat; mortality; nomogram; poisoning; risk prediction model.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
Results of LASSO regression analysis. A. LASSO regression coefficient path; B. Results of LASSO regression cross-validation.
Figure 2
Figure 2
Line chart model for predicting the risk of death in patients with acute diquat poisoning. SIRS, Systemic Inflammatory Response Syndrome; APACHE II, Acute Physiology And Chronic Health Evaluation II.
Figure 3
Figure 3
Validation of the line graph model for predicting mortality risk in patients with acute diquat poisoning. A. The Receiver Operating Characteristic curve (ROC) curve of the nomogram model for predicting the risk of death in patients with acute diquat poisoning; B. The ROC curve of the nomogram model for predicting the risk of death in patients with acute diquat poisoning was drawn by leave-one-out cross-validation. AUC, Area Under Curve.
Figure 4
Figure 4
Correction curve of the line graph model for predicting the risk of death in patients with acute diquat poisoning. A. Correction curve of the training set; B. Calibration curve of the validation set. Pr, Positive Rate.
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