ORIGINAL RESEARCH article
Front. Toxicol.
Sec. Clinical Toxicology
This article is part of the Research TopicEvaluating Toxicological Risks of Traditional Medicines in Modern HealthcareView all 13 articles
Development and validation of a bedside prognostic model for in-hospital mortality in acute diquat poisoning
Provisionally accepted
- 1Beijing Youan Hospital, Capital Medical University, Beijing, China
- 2Characteristic Medical Center of People's Armed Police Force, Tianjin, China
- 3Beijing Mentougou District Hospital, Beijing, China
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Background Despite plasma diquat concentration being the prognostic gold standard in acute diquat poisoning, its utility is limited in resource-constrained settings. We aimed to develop and validate a bedside nomogram using readily accessible clinical variables. Methods This retrospective cohort study included 134 patients with acute diquat poisoning (2016–2025). Using the least absolute shrinkage and selection operator (LASSO) regression , predictors were identified and further validated for independent prognostic effects through multivariable logistic regression. A nomogram was constructed using these variables. The model was developed using the training cohort (n = 81) and subsequently validated on an independent cohort (n = 53).The performance of the nomogram was assessed by bootstrap resampling. The model was evaluated comprehensively via discrimination, calibration [Hosmer-Lemeshow goodness-of-fit test (H-L test)], and clinical utility (decision curve analysis and clinical impact curves). Results The final model, comprising five predictors [white blood cell count (WBC), plasma lactate concentration (Lac), renal insufficiency, respiratory failure, and myocardial injury] identified by LASSO regression and verified by multivariable regression as independent risk factors, demonstrated excellent discrimination [area under receiver operating characteristic curve (AUC-ROC) 0.839 95% confidence interval(CI) 0.754-0.925] in the training set; AUC-ROC 0.874 (95% CI 0.783-0.966) in the validation set) and calibration (P = 0.107 in training; P = 0.824 in validation). Decision curve analysis indicated significant clinical net benefits within threshold probabilities of 5-75% (training) and 4-97% (validation). Clinical impact curves showed the model effectively stratified high-risk patients and accurately captured actual mortality events within these ranges. Conclusions This nomogram, using clinical indicators for mortality risk stratification, may offer decision-making support in resource-limited settings, but its actual value requires prospective study verification.
Keywords: acute diquat poisoning, In-hospital mortality, prognosis, nomogram, prediction
Received: 11 Jul 2025; Accepted: 28 Oct 2025.
Copyright: © 2025 Zhang, Chen, Liu, Du, Jiang and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yingmin Ma, dztangcp@126.com
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