Development and validation of a bedside prognostic model for in-hospital mortality in acute diquat poisoning

Plasma diquat concentration is prognostic in acute poisoning but often unavailable in resource-limited settings. We aimed to develop a bedside model using routine clinical variables for early risk stratification after hospital admission. This retrospective cohort study included 134 patients with acute diquat poisoning (2016–2025). Predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with independent prognostic effects verified by multivariable logistic regression. A nomogram was constructed and developed using a training cohort (n = 81), then validated on a temporally and randomly split validation cohort (n = 53). Model performance was assessed via discrimination [area under the receiver operating characteristic curve (AUC-ROC)], calibration [Hosmer-Lemeshow test], and clinical utility [decision curve analysis (DCA) and clinical impact curves]. Bootstrap optimism correction was performed. The final model comprised five predictors: white blood cell count (WBC), plasma lactate (Lac), renal insufficiency, respiratory failure, and myocardial injury. It demonstrated good discrimination in the training set [AUC-ROC 0.839, 95% confidence interval (CI) 0.754–0.925] and the validation set [AUC-ROC 0.874, 95% CI 0.783–0.966], with satisfactory calibration (P = 0.107 and P = 0.824, respectively). The optimism-corrected C-index was 0.832. DCA suggested potential clinical net benefit within threshold probabilities of 5–75% (training) and 4–97% (validation) in this retrospective simulation. Clinical impact curves showed the model effectively stratified high-risk patients and accurately captured actual mortality events within these ranges. This study presents a clinically accessible nomogram for mortality risk stratification in acute diquat poisoning. Its promising preliminary performance warrants prospective, multi-center validation to confirm clinical utility. Chinese Clinical Trial Registry, ChiCTR2500098079 (retrospectively registered on 3 March 2025).

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Publication Details

Journal
BMC Emergency Medicine
Published
2026-02-16
DOI
https://doi.org/10.1186/s12873-026-01503-0
Primary Topic
Paraquat toxicity studies and treatments
Type
article
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article

Development and validation of a bedside prognostic model for in-hospital mortality in acute diquat poisoning

X. Y. Cai, Xiaoming Jiang, Guoqiang Li, Ye Zhang et al.
BMC Emergency Medicine
Paraquat toxicity studies and treatments
article

Development and validation of a bedside prognostic model for in-hospital mortality in acute diquat poisoning

X. Y. Cai, Xiaoming Jiang, Guoqiang Li, Ye Zhang, Xian Chen, Yingmin Ma, Min Zhao, Haike Du
article en

Abstract

Plasma diquat concentration is prognostic in acute poisoning but often unavailable in resource-limited settings. We aimed to develop a bedside model using routine clinical variables for early risk stratification after hospital admission. This retrospective cohort study included 134 patients with acute diquat poisoning (2016–2025). Predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with independent prognostic effects verified by multivariable logistic regression. A nomogram was constructed and developed using a training cohort (n = 81), then validated on a temporally and randomly split validation cohort (n = 53). Model performance was assessed via discrimination [area under the receiver operating characteristic curve (AUC-ROC)], calibration [Hosmer-Lemeshow test], and clinical utility [decision curve analysis (DCA) and clinical impact curves]. Bootstrap optimism correction was performed. The final model comprised five predictors: white blood cell count (WBC), plasma lactate (Lac), renal insufficiency, respiratory failure, and myocardial injury. It demonstrated good discrimination in the training set [AUC-ROC 0.839, 95% confidence interval (CI) 0.754–0.925] and the validation set [AUC-ROC 0.874, 95% CI 0.783–0.966], with satisfactory calibration (P = 0.107 and P = 0.824, respectively). The optimism-corrected C-index was 0.832. DCA suggested potential clinical net benefit within threshold probabilities of 5–75% (training) and 4–97% (validation) in this retrospective simulation. Clinical impact curves showed the model effectively stratified high-risk patients and accurately captured actual mortality events within these ranges. This study presents a clinically accessible nomogram for mortality risk stratification in acute diquat poisoning. Its promising preliminary performance warrants prospective, multi-center validation to confirm clinical utility. Chinese Clinical Trial Registry, ChiCTR2500098079 (retrospectively registered on 3 March 2025).

BMC Emergency MedicineVol. 26(1)
Beijing Tongren Hospital (CN), Army Medical University (CN), Capital Medical University (CN), Mengo Hospital (UG), Chinese People's Armed Police Force Medical College Affiliated Hospital (CN), Logistics University of People's Armed Police Force (CN), Xinqiao Hospital (CN), Beijing YouAn Hospital (CN)
Openalex Percentile: Top 54%
Paraquat toxicity studies and treatments
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